research topics in financial econometrics

Research Topics & Ideas: Finance

120+ Finance Research Topic Ideas To Fast-Track Your Project

If you’re just starting out exploring potential research topics for your finance-related dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of finance-centric research topics and ideas.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Finance Research Topics

• Corporate finance topics
• Investment banking topics
• Private equity & VC
• Asset management
• Hedge funds
• Financial planning & advisory
• Quantitative finance
• Treasury management
• Financial technology (FinTech)
• Commercial banking
• International finance

Corporate Finance

These research topic ideas explore a breadth of issues ranging from the examination of capital structure to the exploration of financial strategies in mergers and acquisitions.

• Evaluating the impact of capital structure on firm performance across different industries
• Assessing the effectiveness of financial management practices in emerging markets
• A comparative analysis of the cost of capital and financial structure in multinational corporations across different regulatory environments
• Examining how integrating sustainability and CSR initiatives affect a corporation’s financial performance and brand reputation
• Analysing how rigorous financial analysis informs strategic decisions and contributes to corporate growth
• Examining the relationship between corporate governance structures and financial performance
• A comparative analysis of financing strategies among mergers and acquisitions
• Evaluating the importance of financial transparency and its impact on investor relations and trust
• Investigating the role of financial flexibility in strategic investment decisions during economic downturns
• Investigating how different dividend policies affect shareholder value and the firm’s financial performance

Investment Banking

The list below presents a series of research topics exploring the multifaceted dimensions of investment banking, with a particular focus on its evolution following the 2008 financial crisis.

• Analysing the evolution and impact of regulatory frameworks in investment banking post-2008 financial crisis
• Investigating the challenges and opportunities associated with cross-border M&As facilitated by investment banks.
• Evaluating the role of investment banks in facilitating mergers and acquisitions in emerging markets
• Analysing the transformation brought about by digital technologies in the delivery of investment banking services and its effects on efficiency and client satisfaction.
• Evaluating the role of investment banks in promoting sustainable finance and the integration of Environmental, Social, and Governance (ESG) criteria in investment decisions.
• Assessing the impact of technology on the efficiency and effectiveness of investment banking services
• Examining the effectiveness of investment banks in pricing and marketing IPOs, and the subsequent performance of these IPOs in the stock market.
• A comparative analysis of different risk management strategies employed by investment banks
• Examining the relationship between investment banking fees and corporate performance
• A comparative analysis of competitive strategies employed by leading investment banks and their impact on market share and profitability

Private Equity & Venture Capital (VC)

These research topic ideas are centred on venture capital and private equity investments, with a focus on their impact on technological startups, emerging technologies, and broader economic ecosystems.

• Investigating the determinants of successful venture capital investments in tech startups
• Analysing the trends and outcomes of venture capital funding in emerging technologies such as artificial intelligence, blockchain, or clean energy
• Assessing the performance and return on investment of different exit strategies employed by venture capital firms
• Assessing the impact of private equity investments on the financial performance of SMEs
• Analysing the role of venture capital in fostering innovation and entrepreneurship
• Evaluating the exit strategies of private equity firms: A comparative analysis
• Exploring the ethical considerations in private equity and venture capital financing
• Investigating how private equity ownership influences operational efficiency and overall business performance
• Evaluating the effectiveness of corporate governance structures in companies backed by private equity investments
• Examining how the regulatory environment in different regions affects the operations, investments and performance of private equity and venture capital firms

Asset Management

This list includes a range of research topic ideas focused on asset management, probing into the effectiveness of various strategies, the integration of technology, and the alignment with ethical principles among other key dimensions.

• Analysing the effectiveness of different asset allocation strategies in diverse economic environments
• Analysing the methodologies and effectiveness of performance attribution in asset management firms
• Assessing the impact of environmental, social, and governance (ESG) criteria on fund performance
• Examining the role of robo-advisors in modern asset management
• Evaluating how advancements in technology are reshaping portfolio management strategies within asset management firms
• Evaluating the performance persistence of mutual funds and hedge funds
• Investigating the long-term performance of portfolios managed with ethical or socially responsible investing principles
• Investigating the behavioural biases in individual and institutional investment decisions
• Examining the asset allocation strategies employed by pension funds and their impact on long-term fund performance
• Assessing the operational efficiency of asset management firms and its correlation with fund performance

Hedge Funds

Here we explore research topics related to hedge fund operations and strategies, including their implications on corporate governance, financial market stability, and regulatory compliance among other critical facets.

• Assessing the impact of hedge fund activism on corporate governance and financial performance
• Analysing the effectiveness and implications of market-neutral strategies employed by hedge funds
• Investigating how different fee structures impact the performance and investor attraction to hedge funds
• Evaluating the contribution of hedge funds to financial market liquidity and the implications for market stability
• Analysing the risk-return profile of hedge fund strategies during financial crises
• Evaluating the influence of regulatory changes on hedge fund operations and performance
• Examining the level of transparency and disclosure practices in the hedge fund industry and its impact on investor trust and regulatory compliance
• Assessing the contribution of hedge funds to systemic risk in financial markets, and the effectiveness of regulatory measures in mitigating such risks
• Examining the role of hedge funds in financial market stability
• Investigating the determinants of hedge fund success: A comparative analysis

Financial Planning and Advisory

This list explores various research topic ideas related to financial planning, focusing on the effects of financial literacy, the adoption of digital tools, taxation policies, and the role of financial advisors.

• Evaluating the impact of financial literacy on individual financial planning effectiveness
• Analysing how different taxation policies influence financial planning strategies among individuals and businesses
• Evaluating the effectiveness and user adoption of digital tools in modern financial planning practices
• Investigating the adequacy of long-term financial planning strategies in ensuring retirement security
• Assessing the role of financial education in shaping financial planning behaviour among different demographic groups
• Examining the impact of psychological biases on financial planning and decision-making, and strategies to mitigate these biases
• Assessing the behavioural factors influencing financial planning decisions
• Examining the role of financial advisors in managing retirement savings
• A comparative analysis of traditional versus robo-advisory in financial planning
• Investigating the ethics of financial advisory practices

The following list delves into research topics within the insurance sector, touching on the technological transformations, regulatory shifts, and evolving consumer behaviours among other pivotal aspects.

• Analysing the impact of technology adoption on insurance pricing and risk management
• Analysing the influence of Insurtech innovations on the competitive dynamics and consumer choices in insurance markets
• Investigating the factors affecting consumer behaviour in insurance product selection and the role of digital channels in influencing decisions
• Assessing the effect of regulatory changes on insurance product offerings
• Examining the determinants of insurance penetration in emerging markets
• Evaluating the operational efficiency of claims management processes in insurance companies and its impact on customer satisfaction
• Examining the evolution and effectiveness of risk assessment models used in insurance underwriting and their impact on pricing and coverage
• Evaluating the role of insurance in financial stability and economic development
• Investigating the impact of climate change on insurance models and products
• Exploring the challenges and opportunities in underwriting cyber insurance in the face of evolving cyber threats and regulations

Quantitative Finance

These topic ideas span the development of asset pricing models, evaluation of machine learning algorithms, and the exploration of ethical implications among other pivotal areas.

• Developing and testing new quantitative models for asset pricing
• Analysing the effectiveness and limitations of machine learning algorithms in predicting financial market movements
• Assessing the effectiveness of various risk management techniques in quantitative finance
• Evaluating the advancements in portfolio optimisation techniques and their impact on risk-adjusted returns
• Evaluating the impact of high-frequency trading on market efficiency and stability
• Investigating the influence of algorithmic trading strategies on market efficiency and liquidity
• Examining the risk parity approach in asset allocation and its effectiveness in different market conditions
• Examining the application of machine learning and artificial intelligence in quantitative financial analysis
• Investigating the ethical implications of quantitative financial innovations
• Assessing the profitability and market impact of statistical arbitrage strategies considering different market microstructures

Treasury Management

The following topic ideas explore treasury management, focusing on modernisation through technological advancements, the impact on firm liquidity, and the intertwined relationship with corporate governance among other crucial areas.

• Analysing the impact of treasury management practices on firm liquidity and profitability
• Analysing the role of automation in enhancing operational efficiency and strategic decision-making in treasury management
• Evaluating the effectiveness of various cash management strategies in multinational corporations
• Investigating the potential of blockchain technology in streamlining treasury operations and enhancing transparency
• Examining the role of treasury management in mitigating financial risks
• Evaluating the accuracy and effectiveness of various cash flow forecasting techniques employed in treasury management
• Assessing the impact of technological advancements on treasury management operations
• Examining the effectiveness of different foreign exchange risk management strategies employed by treasury managers in multinational corporations
• Assessing the impact of regulatory compliance requirements on the operational and strategic aspects of treasury management
• Investigating the relationship between treasury management and corporate governance

Financial Technology (FinTech)

The following research topic ideas explore the transformative potential of blockchain, the rise of open banking, and the burgeoning landscape of peer-to-peer lending among other focal areas.

• Evaluating the impact of blockchain technology on financial services
• Investigating the implications of open banking on consumer data privacy and financial services competition
• Assessing the role of FinTech in financial inclusion in emerging markets
• Analysing the role of peer-to-peer lending platforms in promoting financial inclusion and their impact on traditional banking systems
• Examining the cybersecurity challenges faced by FinTech firms and the regulatory measures to ensure data protection and financial stability
• Examining the regulatory challenges and opportunities in the FinTech ecosystem
• Assessing the impact of artificial intelligence on the delivery of financial services, customer experience, and operational efficiency within FinTech firms
• Analysing the adoption and impact of cryptocurrencies on traditional financial systems
• Investigating the determinants of success for FinTech startups

Commercial Banking

These topic ideas span commercial banking, encompassing digital transformation, support for small and medium-sized enterprises (SMEs), and the evolving regulatory and competitive landscape among other key themes.

• Assessing the impact of digital transformation on commercial banking services and competitiveness
• Analysing the impact of digital transformation on customer experience and operational efficiency in commercial banking
• Evaluating the role of commercial banks in supporting small and medium-sized enterprises (SMEs)
• Investigating the effectiveness of credit risk management practices and their impact on bank profitability and financial stability
• Examining the relationship between commercial banking practices and financial stability
• Evaluating the implications of open banking frameworks on the competitive landscape and service innovation in commercial banking
• Assessing how regulatory changes affect lending practices and risk appetite of commercial banks
• Examining how commercial banks are adapting their strategies in response to competition from FinTech firms and changing consumer preferences
• Analysing the impact of regulatory compliance on commercial banking operations
• Investigating the determinants of customer satisfaction and loyalty in commercial banking

International Finance

The folowing research topic ideas are centred around international finance and global economic dynamics, delving into aspects like exchange rate fluctuations, international financial regulations, and the role of international financial institutions among other pivotal areas.

• Analysing the determinants of exchange rate fluctuations and their impact on international trade
• Analysing the influence of global trade agreements on international financial flows and foreign direct investments
• Evaluating the effectiveness of international portfolio diversification strategies in mitigating risks and enhancing returns
• Evaluating the role of international financial institutions in global financial stability
• Investigating the role and implications of offshore financial centres on international financial stability and regulatory harmonisation
• Examining the impact of global financial crises on emerging market economies
• Examining the challenges and regulatory frameworks associated with cross-border banking operations
• Assessing the effectiveness of international financial regulations
• Investigating the challenges and opportunities of cross-border mergers and acquisitions

Choosing A Research Topic

These finance-related research topic ideas are starting points to guide your thinking. They are intentionally very broad and open-ended. By engaging with the currently literature in your field of interest, you’ll be able to narrow down your focus to a specific research gap .

When choosing a topic , you’ll need to take into account its originality, relevance, feasibility, and the resources you have at your disposal. Make sure to align your interest and expertise in the subject with your university program’s specific requirements. Always consult your academic advisor to ensure that your chosen topic not only meets the academic criteria but also provides a valuable contribution to the field. 

If you need a helping hand, feel free to check out our private coaching service here.

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102 Best Econometrics Research Topics

College and university professors require students to write about econometrics research topics to gauge their comprehension of the relationship between mathematical economics, statistics, and economics.

The purpose of this integration is to provide numerical values to economic relationships and parameters. Usually, econometrics involves economic theories and their presentation in mathematical forms and the empirical study of business. Perhaps, this integration explains why some students struggle to choose topics for research in econometrics.

What Is Econometrics?

As hinted, econometrics is an economics branch that focuses on the relationships between economics, statistics, and mathematical economics. Ideally, econometrics entails the quantitative application of mathematical and statistical models using data to test hypotheses and develop economic theories while forecasting future trends based on historical data. Econometricians subject real-world data to various statistical trials while comparing and contrasting the results against the idea under examination.

Writing an econometric research paper is a process that starts with the selection of an interesting topic. Once you’ve chosen a title and the supervisor approves it, embark on extensive research using the prompt from your teacher. Proceed by gathering and analyzing all relevant information from different sources. Engaging in in-depth study and comprehensive analysis will enable you to write an informative paper that will compel the educator to award you the best grade in your class. Below are the steps to follow to write a high-quality econometric thesis or essay.

Write the introduction: Introduce your econometrics topic and tell the audience why it’s crucial. Also, include a thesis statement summarizing the entire paper. Describe the theoretical model: Tell the readers about the theoretical models to structure the empirical work. Present the data: Describe the data, whether time series or cross-sectional. Use descriptive statistics data and graphics if possible. Present the empirical model: Explain the model you intend to estimate and the functional form you intend to use. Present your empirical results: This section presents empirical results using a table to summarize them. Conclude the paper: Describe lessons from the research and state whether it supports the theory. Also, suggest approaches for future research on the topic.

Your paper should also include a reference section comprising the information sources you used to gather data.

Interesting Econometrics Paper Topics

Maybe you know the process of writing a paper on an econometrics topic but don’t have an idea to explore. If so, consider these exciting econometrics paper ideas.

• How privatizing public enterprises could affect economic development and policy
• Cashless economy: How demonetization affects medium and small businesses
• How Gini index dynamics reflect the income inequality problem
• Consumption evolution over the last decade: Consumer behavior and trends
• Investigating salary inequalities and the forces behind them
• How income changes affect consumer choices
• How does allowing the labor force to participate in public budgeting affect the economy
• How the marital status affect the labor force composition
• How consumption attitudes have changed over the last decade
• How economic convergence relates to salary levels
• How income affects life insurance
• The consequences of leaving the rat race
• Testing Okun’s Law in the U.S
• Analysis of spending on disposable income and imports
• Comparing the unemployment rate in the United States to the rest of the world
• Regional labor mobility and unemployment
• Stock market evolution: Analyzing the causes and effects
• How internet productivity relate to connectivity in the workplace
• How currency devaluation affects medium and small companies
• How government spending and inflation relate in an economy
• The relationship between stock prices and inflation in a country
• How income tax revenue affects a developing economy
• How government expenditure affects economic growth
• Factors contributing to the global recession
• How a country’s unemployment rate relates to economic growth

Any of these topics can be an excellent basis for an econometrics paper. However, you require extensive research about any of these topics to develop a winning thesis.

Undergraduate Econometrics Project Ideas

Maybe your school or faculty requires you to write an econometrics paper to graduate from university. In that case, consider these econometrics research topics for undergraduates.

• Analyzing the impact of income inequality on the poverty level
• Analyzing gender differences in education between developing and developed countries
• How immigration affects unemployment in the European Union
• How economic growth relates to trade
• Are immigrants more in countries with a high income?
• How high taxations affect GSP
• Analyzing the relationship between local income level and house prices
• How income, education, and life expectancy affect the human development index
• How inflation affects national savings
• How life expectancy relates to national income
• How financial development affects the economic growth of a country
• Crime index versus the average education years
• Investigating the correlation between youth unemployment and minimum wage
• How economic prosperity relate to government systems
• Economic factors that affect housing prices in the United States
• Economic factors contributing to homelessness in the U.S
• Socioeconomic and economic determinants of infant mortality
• Econometric analysis: Impact of trade barriers
• Why matching methods are essential in econometrics
• How a randomized experiment can aid econometrics
• Why instrumental variables matter in econometrics
• Can experts predict the future using econometrics?
• Econometrics as a numerical estimates source for economic relationship variables
• Ways of testing economic theories that econometricians present
• Regression discontinuity: Describe its application

These are great ideas to consider for an econometrics project. Nevertheless, you require sufficient time to research any of these topics and write a winning essay or dissertation.

Easy Econometric Research Topics

Perhaps, you need an easy topic for an econometrics paper. Maybe you have a short time to complete your assignment. In that case, these econometrics topics are ideal for you.

• Theory suggestion- The initial econometrics methodology step
• Why estimating variables is important
• The importance of Proof-reading once you have evaluated the variables
• Why testing a hypothesis matters
• The impact of poverty on education
• How poverty relates to childhood obesity
• Human development and income inequality
• The link between religion and ideologies on a country’s economy
• Income and importation- How do they connect?
• Personal income and life expectancy- What is the connection?
• The effects of minimum wage on unemployment
• Investigating monetary policies and bank regulations
• A study of the economies of scale
• The impact and relevance of comparative institutional economics
• Analyzing the effect of making a company international
• Studying the macroeconomics of rare events
• Investigating customer behavior towards green products
• Trade patterns: Investigating different trade patterns and their applications
• Different stochastic processes concepts
• Accurate stochastic processes prediction

Any of these topics can be a sound basis of a simple paper. Nevertheless, you still require time to research the idea and analyze data to develop a quality paper.

Financial Econometrics Research Paper Topics

Perhaps, you want to write an academic paper about a financial econometrics topic. If so, consider these ideas.

• How does bank regulation affect the economy?
• A critical look into the loan markets
• How a cashless policy affects the economy
• Structure and implementation of the monetary policy
• Lessons to learn from financial crises
• Investigating regression models
• Statistical tools in the financial econometrics

These are good topics to explore in financial econometrics. However, follow the prompt from your teacher to write an impressive paper.

Econometrics Empirical Project Ideas FExor Ph.D. Level

Maybe you’re pursuing your Ph.D. and want to write a dissertation about an econometrics topic. In that case, this category comprises excellent ideas for you.

• Analytical statistics versus theoretical statistics
• The effects of the low and high demand of labor on an economy
• The arbitrage pricing theory
• How goods production and productivity affect econometrics at a national level
• Applied econometrics- Its essence in turning qualitative economic ideas into quantitative ones
• Definition, relevance, and application of the general line model
• Theoretical econometrics’ study and application
• The macro behavioral theory
• Panel data methods applications- A microeconomics subsection
• The impacts of the spillover effect on econometrics
• The impact of labor supply on a local economy
• Why labor markets are essential to econometrics
• What is micro-econometrics modeling?
• Micro-econometrics methods and applications
• Statistical tools and their use in financial econometrics

This list also has fantastic economics paper topic ideas. But like the topics in the other sections, each of these notions requires extensive research to write a quality paper.

Exciting Econometrics Questions

Maybe you need a question to serve as the basis of your econometrics research. In that case, here are exciting queries to inspire you.

• What is the current state of your country’s economy?
• What’s the difference between the current state of the local and international trades?
• What are the latest forecasts for the global economy?
• How do the foreign exchange market and the local businesses relate?
• What’s the impact of exportation and importation on the local economy?
• How do businesses monopolies affect a country’s economy?
• What are the effects of international banks on the local banking sector?
• How does population growth affect economic development?
• How can a natural disaster affect an emerging economy?
• What techniques do companies use to “nudge” consumers into spending more?

This comprehensive list has some of the best econometrics ideas for essays and research papers. Nevertheless, having a topic is not a guarantee that you’ll write a good essay. You might still need help with your assignment after choosing a topic.

Get Help With Thesis About Econometrics Topic

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70 Econometrics Research Topics for You

Econometrics is an interesting area of research. However, picking econometric research topics may be a bit difficult. This is because it is a broad aspect of economics with increasing relevance by the day.

Economic relationships can be accurately measured and defined. This involves a certain statistical and mathematical approach known as econometrics. It is the foundation for economic forecasting.

In this article, you will find 70 interesting econometrics topics crafted for you! We have done the hard work, saving you the stress of picking the right topics for your presentation, research work, or seminar. Are you ready? Let’s get started!

Easy Econometrics Paper Topics

Need to write an econometrics research paper? Here are some econometrics research paper topics for you!

• The suggestion of theory- the initial step in econometrics methodology
• How Inflation affects National Savings
• Estimating variables- why it must be done
• The essence of Proof-reading after estimating variables
• Why the need for testing a hypothesis
• How Trade Relates to Economic growth: An in-depth look.
• Analyzing how Income Affects Life Insurance

Econometrics Research Topics for Undergraduates

Need to write an undergraduate econometrics research paper? Here are some econometrics topics suitable for undergraduate research purposes.

• How poverty affects education
• Relationship between childhood obesity and poverty
• Income inequality and human development
• The link between ideologies and religion on the economy of a country
• Importation and income- what is the relationship?
• Life expectancy and personal income- the link
• How minimum wage affects unemployment

Econometrics Topics for a Project

If you are studying something around econometrics at college, you’ll need to do a project! Here are some econometrics research paper topics for your project!

• Can econometrics be used to predict the future?
• Econometrics is a source of numerical estimates for the variables of economic relationships
• How to test economic theories presented by econometricians using econometrics
• What is regression discontinuity, and how can it be applied?
• The role of instrumental variables in econometrics
• How randomized experiments aid econometrics
• The essence of matching methods in econometrics

Interesting Econometrics Research Topics

If econometric research is one of the things that gives you goosebumps, we have added to your excitement. We just saved you the stress of having to look for interesting econometrics research topics. Here are 7 interesting econometrics topics for you!

• A study of customer behavior for green products
• Trade patterns- a research on the various kinds and their applications today
• A study on rare events and their macroeconomics
• A study of the effect of making a business international
• Comparative institutional economics- relevance and impact
• Research on the economies of scale
• A close look at bank regulations and monetary policies

Sports Econometrics Topics

The economics of sport is analyzed using sports econometrics. This makes it easier to make forecasts and accurate predictions. We have selected interesting econometrics topics in the sports niche for you. See them here:

• How a sports team affects the local economy
• The impact of discrimination in sports
• Sports attendance and its economic relevance
• Factors that affect competitive balance in sports and its effect on the economy
• The relationship between threshold efficiency and market competition in sports
• The economics of professional football contracts
• Professional hockey- skill, performance, and earnings

Financial Econometrics Research Topics

There are numerous topics for econometrics research papers. We have made it hassle-free for you to pick one for your next research work. Here are some econometrics topics research papers below.

• Latest statistical tools for financial econometrics
• A study on multiple regression model and its applications
• What to learn from recent financial crises
• Monetary policy- structuring and implementation
• Cashless policy and its impact on the economy
• The loan markets-A critical look and survey
• Bank regulation and policy-the impact on the economy

Micro Econometrics Topics Suggestions

Micro econometrics is an interesting area to make a presentation. To this end, we have picked out seven relevant topics in econometrics that pertain to micro econometrics. See them below!

• The methods and applications of micro econometrics
• Micro econometric modeling
• The role of the labor market in econometrics
• How labor supply impacts the local economy
• What is the spillover effect, and how does it affect econometrics?
• The application of panel data methods- a subsection of microeconomics
• Micro behavioral theory

Advanced Topics in Econometrics

Need some advanced econometrics topics? Here are some advanced econometrics research topics just for you.

• The study and application of theoretical econometrics
• General line model- definition, application, and relevance
• Applied econometrics- the key to converting qualitative economic statements into quantitative ones
• How productivity and goods production affects econometrics at the national level
• The theory of arbitrage pricing
• The impact of high or low demand of labor on the economy
• Theoretical statistics vs. Analytical statistics

Good Simple Econometric Research Topics

Need some good econometrics topics that are easy to work on? Below are some simple topics in econometrics for your research!

• The various concepts of stochastic processes
• Accurate prediction of stochastic processes
• Line time series model- its definition, estimation, and application
• The purpose of dynamic econometric models
• Multiple time series model- a critical approach to determine its application
• The relevance of vector autoregressive processes
• Causality and impulse-response analysis

Receive Your Econometrics Research Paper

Want someone to hand over simple research paper topics to you? The following are econometric paper topics selected for you!

• The state of the national economy
• InterInternal trades and local economic status
• The impact of importation and exportation on the local economy
• Supply and demand forecast
• A critique on international banks
• The foreign exchange market-how it relates to local businesses
• Business monopolies in the nation- a critical study

If you’re pursuing a thesis in the field of econometrics and find yourself in need of expert support, you might want to consider seeking professional assistance for writing your thesis. Our reputable academic writing services provide specialized help that can alleviate the challenges you may encounter during the thesis writing process. By requesting us to ‘ write my thesis for me ,’ you can tap into the expertise of experienced writers who possess in-depth knowledge of econometrics. Our experts will guide you through the intricacies of data analysis, statistical modeling, and hypothesis testing, ensuring that your thesis adheres to the highest academic standards

We believe you now have more than enough topics in econometrics in your arsenal. With these interesting econometric topics, you can make the best seminar presentations or research around econometrics! Have fun!

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Financial Economics: Definition, Topics, Methods, and Importance

Daniel Liberto is a journalist with over 10 years of experience working with publications such as the Financial Times, The Independent, and Investors Chronicle.

Erika Rasure is globally-recognized as a leading consumer economics subject matter expert, researcher, and educator. She is a financial therapist and transformational coach, with a special interest in helping women learn how to invest.

What Is Financial Economics?

Financial economics is a branch of economics that analyzes the use and distribution of resources in markets . Financial decisions must often take into account future events, whether those be related to individual stocks,  portfolios , or the market as a whole.

Key Takeaways

• Financial economics analyzes the use and distribution of resources in markets.
• It employs economic theory to evaluate how time, risk, opportunity costs, and information can create incentives or disincentives for a particular decision.
• Financial economics often involves the creation of sophisticated models to test the variables affecting a particular decision.

How Financial Economics Works

Making financial decisions is not always a straightforward process. Time, risk (uncertainty),  opportunity costs , and information can create incentives or disincentives. Financial economics employs economic theory to evaluate how certain things impact decision making, providing investors with the instruments to make the right calls.

Financial economics usually involves the creation of sophisticated models to test the variables affecting a particular decision. Often, these models assume that individuals or institutions making decisions act rationally, though this is not necessarily the case. The irrational behavior of parties has to be taken into account in financial economics as a potential risk factor.

This branch of economics builds heavily on microeconomics and basic accounting concepts. It is a quantitative discipline that uses econometrics as well as other mathematical tools.

Financial economics necessitates familiarity with basic probability and statistics since these are the standard tools used to measure and evaluate risk. 

Financial economics studies fair value, risk and returns, and the financing of securities and assets. Numerous monetary factors are taken into account, too, including interest rates and inflation.

Financial Economics vs. Traditional Economics

Traditional economics focuses on exchanges in which money is one — but only one — of the items traded. In contrast, financial economics concentrates on exchanges in which money of one type or another is likely to appear on both sides of a trade. 

The financial economist can be distinguished from traditional economists by their focus on monetary activities in which time, uncertainty, options and information play roles. 

Financial Economics Methods

There are many angles to the concept of financial economics. Two of the most prominent are:

Discounting

Decision making over time recognizes the fact that the value of $1 in 10 years' time is less than the value of $1 now. Therefore, the $1 at 10 years must be discounted to allow for risk, inflation , and the simple fact that it is in the future. Failure to discount appropriately can lead to problems, such as underfunded pension schemes.

Risk Management and Diversification

Advertisements for stock market-based financial products must remind potential buyers that the value of investments may fall as well as rise.

Financial institutions are always looking for ways of insuring, or hedging , this risk. It is sometimes possible to hold two highly risky assets but for the overall risk to be low: if share A only performs badly when share B performs well (and vice versa) then the two shares perform a perfect hedge.

An important part of finance is working out the total risk of a portfolio of risky assets, since the total risk may be less than the risk of the individual components.

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Econometrics Research Paper Topics

This page provides an extensive list of econometrics research paper topics divided into 10 categories, expert advice on topic selection, and valuable insights on how to write a successful econometrics research paper. Additionally, the page introduces the writing services provided by iResearchNet, offering students the opportunity to order custom econometrics research papers on any topic. With expert writers, in-depth research, and top-quality custom papers, iResearchNet ensures a seamless and efficient experience for students seeking assistance in their econometrics research endeavors.

100 Econometrics Research Paper Topics

Welcome to the comprehensive list of econometrics research paper topics. As a student of economics, you may be tasked with writing a research paper that delves into the world of econometrics—the application of statistical methods to economic data. This list is designed to provide you with a wide range of research paper topics in econometrics, divided into 10 categories. Each category offers a collection of 10 unique and engaging topics that cover various aspects of econometric analysis. Whether you are interested in time series analysis, cross-sectional analysis, applied microeconometrics, or any other subfield of econometrics, you will find a plethora of intriguing research ideas to explore. So, let’s dive into this rich collection of econometrics research paper topics and discover the fascinating intersection between economics and statistical analysis.

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Time Series Analysis Research Paper Topics:

• Modeling and forecasting stock market returns using time series econometric models
• The impact of macroeconomic variables on exchange rates: A time series analysis
• Analyzing volatility in financial markets using ARCH/GARCH models
• Econometric modeling of economic growth: A time series approach
• Cointegration analysis and its implications for long-run equilibrium relationships
• Autoregressive integrated moving average (ARIMA) models in economic forecasting
• Analysis of panel data and fixed effects models in time series econometrics
• Dynamic factor models and their applications in time series analysis
• Nonlinear time series analysis and its relevance in understanding economic dynamics
• Estimation and testing in time series econometrics: Challenges and advancements

Cross-Sectional Analysis Research Paper Topics:

• Determinants of firm performance using panel data and econometric techniques
• Analysis of income inequality and its drivers: A cross-sectional econometric study
• Impact evaluation of public policies using experimental and quasi-experimental designs
• Quantile regression and its applications in cross-sectional analysis
• The relationship between education and earnings: An econometric examination
• Analyzing labor market outcomes and human capital using cross-sectional data
• Modeling consumer behavior and demand estimation: A cross-sectional perspective
• Spatial econometrics and its relevance in studying regional disparities
• Health economics and econometric analysis of healthcare outcomes
• Estimating production functions and efficiency analysis using cross-sectional data

Panel Data Analysis Research Paper Topics:

• Fixed effects versus random effects models: A comparative analysis
• Dynamics of financial contagion: A panel data approach
• Analysis of firm-level productivity using panel data econometric techniques
• The impact of foreign direct investment on economic growth: A panel data analysis
• Panel cointegration analysis and its implications for long-run relationships
• Endogeneity and instrumental variable approaches in panel data analysis
• Analysis of trade flows and gravity models using panel data econometrics
• Panel data analysis of environmental sustainability and economic development
• The role of institutions in economic performance: A panel data study
• Dynamic panel data models and their applications in economic research

Applied Microeconometrics Research Paper Topics:

• The impact of minimum wage policies on employment: An empirical analysis
• Estimating demand elasticity for specific goods: A microeconometric approach
• Analysis of consumer behavior and choice modeling using microeconometric techniques
• The effects of government interventions on market outcomes: A microeconometric study
• Estimating the returns to education using microeconometric methods
• Labor market outcomes and the impact of training programs: An empirical investigation
• Health economics and the evaluation of healthcare interventions: A microeconometric perspective
• The role of social networks in economic outcomes: An applied microeconometric analysis
• Econometric analysis of industrial organization and market structure
• Discrete choice modeling and its applications in empirical microeconomics

Applied Macroeconometrics Research Paper Topics:

• The role of monetary policy in stabilizing the economy: An applied macroeconometric analysis
• Estimating fiscal multipliers and the impact of government spending on economic growth
• Time-varying parameter models in macroeconometrics: Theory and applications
• The effects of exchange rate fluctuations on trade: An empirical investigation
• Macroeconomic forecasting using state-space models and Bayesian econometrics
• Empirical analysis of business cycles and their synchronization across countries
• The impact of financial shocks on macroeconomic stability: An econometric perspective
• Dynamic stochastic general equilibrium (DSGE) models and their applications in macroeconomics
• The role of expectations in shaping macroeconomic outcomes: An econometric study
• The interaction between monetary and fiscal policy: Empirical evidence and policy implications

Financial Econometrics Research Paper Topics:

• Estimating risk and return in financial markets using econometric models
• Analyzing the efficiency of financial markets using event study methodologies
• GARCH models and volatility forecasting in financial econometrics
• The impact of macroeconomic factors on stock market performance: An empirical analysis
• Value-at-Risk (VaR) modeling and its applications in risk management
• Asset pricing models and their empirical validation using financial econometric techniques
• Credit risk modeling and default prediction: An econometric perspective
• High-frequency financial data analysis and its implications for trading strategies
• The relationship between interest rates and stock market returns: A financial econometric study
• Econometric analysis of financial contagion and systemic risk in banking

Applied Econometrics in Development Economics Research Paper Topics:

• Impact evaluation of development programs using randomized controlled trials (RCTs)
• Estimating the effects of foreign aid on economic growth: An applied econometric analysis
• The role of institutions in development: An empirical investigation
• Analyzing the determinants of poverty and inequality using applied econometric techniques
• Econometric analysis of migration and its impact on development outcomes
• The effects of trade liberalization on economic development: An empirical examination
• Financial inclusion and its relationship with economic development: An applied econometric study
• Empirical analysis of social protection programs and their impact on poverty reduction
• Evaluating the effectiveness of education policies in promoting human capital development
• Econometric analysis of infrastructure investment and its contribution to economic development

 Environmental and Resource Economics Research Paper Topics:

• Econometric modeling of environmental pollution and its impact on health outcomes
• Valuation of ecosystem services and their contribution to economic welfare: An applied econometric approach
• The effects of climate change on agricultural productivity: A resource economics perspective
• Empirical analysis of renewable energy policies and their impact on economic sustainability
• Dynamic optimization models in environmental economics and resource management
• Econometric analysis of water resource allocation and its implications for economic efficiency
• The role of market-based instruments in environmental regulation: An empirical examination
• The impact of deforestation on economic development and sustainability: An econometric study
• Non-market valuation of environmental goods and ecosystem conservation: An applied econometric analysis
• Econometric analysis of environmental policy effectiveness and its implications for sustainable development

International Economics Research Paper Topics:

• Empirical analysis of trade patterns and globalization using international econometric techniques
• Exchange rate regimes and their impact on trade and economic stability: An econometric analysis
• The role of foreign direct investment in economic growth: An empirical investigation
• Gravity models and their applications in international trade analysis
• Trade liberalization and its effects on labor markets: An applied international econometric study
• The impact of trade policies on income distribution: A cross-country analysis
• Empirical analysis of exchange rate pass-through and its implications for import and export prices
• The relationship between financial integration and economic growth: An international econometric perspective
• Economic convergence and divergence across countries: An econometric examination
• The effects of trade agreements on trade flows and economic integration: An empirical analysis

Applied Econometrics in Public Finance Research Paper Topics:

• The impact of tax policies on economic behavior: An applied econometric study
• Analyzing the effectiveness of government expenditure on public goods: An empirical investigation
• Fiscal sustainability and the implications for long-run economic growth: An econometric analysis
• Estimating the incidence and distributional effects of taxation using applied econometric techniques
• Empirical analysis of government debt and its implications for macroeconomic stability
• The role of public investment in fostering economic development: An applied econometric study
• Econometric analysis of the determinants of tax compliance and tax evasion
• The effects of social welfare programs on labor market outcomes: An empirical examination
• Evaluating the impact of education spending on human capital development: An econometric analysis
• The relationship between government size and economic performance: An applied econometric study

Econometrics Research Paper Writing Guide

Welcome to the world of econometrics research! As an economics student, you have embarked on a journey to explore the fascinating realm of econometric analysis and its application in the field of economics. Writing a research paper in econometrics provides you with an opportunity to delve deeper into this quantitative discipline and contribute to the existing body of knowledge.

This page serves as a comprehensive guide specifically tailored for economics students who are tasked with writing an econometrics research paper. Whether you are new to econometrics or seeking to enhance your understanding and skills in this field, this guide will provide you with valuable insights and resources to navigate the process effectively.

Within this page, you will find an abstract that encapsulates the essence of exploring econometrics research paper topics, followed by a comprehensive list of research topics divided into 10 categories. Additionally, expert advice on selecting suitable topics and valuable guidance on how to write an econometrics research paper will be provided to support you throughout your research journey.

To further assist you in your academic endeavors, iResearchNet offers custom writing services where you can order a custom econometrics research paper tailored to your specific requirements. With a team of expert degree-holding writers, in-depth research capabilities, and a commitment to top-quality customized solutions, iResearchNet is dedicated to providing you with a seamless and successful research paper writing experience.

Let’s dive into the world of econometrics research, explore intriguing research topics, and equip ourselves with the knowledge and skills to excel in this exciting field.

How to Choose an Econometrics Topic

Choosing the right econometrics research paper topic is crucial to the success of your study. With the vast array of possibilities within the field of econometrics, it can be challenging to narrow down your options and select a topic that is both interesting and feasible. To help you navigate through this process, we have compiled some expert advice on choosing econometrics research paper topics:

• Identify your research interests : Begin by reflecting on your personal interests within the field of economics. Are you fascinated by macroeconomic issues, labor markets, financial econometrics, or perhaps environmental economics? Understanding your areas of passion will guide you towards selecting a research topic that aligns with your interests and motivates you throughout the research process.
• Review current literature : Familiarize yourself with the existing literature in econometrics, especially recent publications and studies. This will not only deepen your understanding of the subject but also give you insights into the latest research trends and gaps in knowledge. Look for areas where you can contribute new perspectives or address unanswered questions.
• Consider data availability : Before finalizing your research topic, assess the availability of relevant data. Econometrics heavily relies on empirical analysis, and having access to high-quality and appropriate datasets is crucial. Ensure that you can obtain the necessary data for your research topic and assess its suitability for your analysis.
• Narrow down your scope : Econometrics research can range from broad macroeconomic studies to more focused microeconometric analyses. Consider the scope of your research and determine whether you want to explore a specific aspect within a broader context or delve deep into a specific area. Narrowing down your scope will allow you to conduct a more focused and impactful study.
• Consult with your advisor : Seek guidance from your advisor or faculty members who have expertise in econometrics. They can provide valuable insights, suggest potential research areas, and help you refine your research question. Their expertise and experience will be invaluable in ensuring that your research topic is viable and aligns with current research trends.
• Balance complexity and feasibility : While it’s important to choose a topic that challenges you academically, it’s equally crucial to consider the feasibility of your research within the available timeframe and resources. Strive for a balance between a topic that pushes the boundaries of knowledge and one that is realistic to accomplish within the given constraints.
• Brainstorm and refine : Engage in brainstorming sessions where you jot down ideas, concepts, and potential research questions. Refine these ideas by evaluating their significance, relevance, and feasibility. Consider the potential impact of your research and its contribution to the field of econometrics.

Remember, the key to choosing a successful econometrics research paper topic is finding a balance between your interests, the availability of data, the scope of your research, and the guidance of experts. By carefully considering these factors and leveraging the expertise of your advisors, you can select a compelling research topic that will drive your study forward.

How to Write an Econometrics Research Paper

Writing an econometrics research paper requires a systematic approach to effectively communicate your findings and contribute to the field of economics. Whether you are a seasoned researcher or new to the world of econometrics, the following guidelines will help you navigate the process of writing an impactful econometrics research paper:

• Understand the research question : Start by clearly defining your research question or objective. What specific problem or issue are you addressing? Ensure that your research question is focused, well-defined, and aligns with the broader scope of econometrics.
• Review relevant literature : Conduct a comprehensive review of existing literature related to your research topic. Familiarize yourself with the theories, methodologies, and empirical findings that are relevant to your research question. This will provide a strong foundation for your study and help you position your research within the existing body of knowledge.
• Develop a theoretical framework : Construct a theoretical framework that outlines the concepts, relationships, and hypotheses underlying your research. Identify the key variables and their expected effects. The theoretical framework will guide your empirical analysis and provide a coherent structure for your paper.
• Choose appropriate econometric techniques : Select the appropriate econometric techniques and models to analyze your data and test your hypotheses. Consider whether your research requires time series analysis, panel data analysis, cross-sectional analysis, or a combination of these approaches. Justify your choice of econometric techniques and explain how they align with your research objectives.
• Collect and preprocess data : Gather the necessary data for your analysis. Ensure that the data is reliable, relevant, and suitable for econometric analysis. Preprocess the data by cleaning, organizing, and transforming it as needed. Document the steps taken in data collection and preprocessing to ensure transparency and reproducibility.
• Conduct empirical analysis : Apply the selected econometric techniques to analyze your data. Present the empirical results in a clear and concise manner, using appropriate tables, graphs, and statistical measures. Interpret the results in the context of your research question and theoretical framework. Discuss any limitations or challenges encountered during the empirical analysis.
• Discuss implications and policy recommendations : Evaluate the implications of your findings and their relevance to the broader field of economics. Discuss the significance of your results in addressing the research question and advancing economic knowledge. If applicable, provide policy recommendations based on your empirical analysis.
• Structure your paper : Organize your research paper into sections that flow logically and coherently. Typically, an econometrics research paper includes an introduction, literature review, theoretical framework, data and methodology, empirical analysis, results, discussion, conclusion, and references. Follow a clear and concise writing style, using appropriate terminology and providing sufficient details for readers to understand and replicate your study.
• Revise and edit : After completing the initial draft, carefully review and revise your research paper. Pay attention to the clarity of your arguments, the coherence of your ideas, and the accuracy of your findings. Proofread for grammar, spelling, and formatting errors. Seek feedback from peers or mentors to gain additional perspectives and suggestions for improvement.
• Cite sources properly : Accurately cite all the sources you have referenced in your research paper using an appropriate citation style, such as APA, MLA, or Chicago. Adhere to ethical guidelines and avoid plagiarism by properly attributing the ideas, theories, and empirical results of others.

By following these guidelines, you can effectively structure and communicate your econometrics research findings. Remember to stay focused, provide clear explanations, and contribute to the advancement of econometric knowledge in your field.

Order a Custom Research Paper

When it comes to writing an econometrics research paper, you may encounter challenges that require expert assistance. If you find yourself struggling with time constraints, complex data analysis, or the intricacies of econometric models, consider leveraging the writing services offered by iResearchNet. Our team of experienced writers and researchers can provide you with a custom econometrics research paper tailored to your specific requirements. Here are the key features of our writing services:

• Expert degree-holding writers : Our team consists of writers with advanced degrees in economics and expertise in econometrics. They possess the knowledge and skills to conduct rigorous research, analyze data, and write high-quality econometrics research papers.
• Custom written works : We understand the importance of originality and customization. Your research paper will be crafted from scratch, based on your specific instructions, research question, and data. We ensure that each paper is unique and tailored to meet your academic needs.
• In-depth research : Our writers are adept at conducting thorough and comprehensive research. They have access to a wide range of reputable sources, databases, and econometric software tools to gather relevant information and empirical data for your research paper.
• Custom formatting : We adhere to the formatting style of your choice, whether it’s APA, MLA, Chicago/Turabian, or Harvard. Our writers are well-versed in the specific requirements of each formatting style and will ensure that your research paper follows the prescribed guidelines.
• Top quality and customized solutions : We prioritize quality and strive to deliver research papers that meet the highest academic standards. Our writers pay meticulous attention to detail, ensuring that your paper is well-structured, coherent, and effectively communicates your research findings.
• Flexible pricing : We offer competitive and flexible pricing options to accommodate the budgetary constraints of students. Our pricing structure takes into account factors such as the complexity of the research paper, the urgency of the deadline, and the desired level of customization.
• Short deadlines : We understand that students often face tight deadlines. Our writing services offer short turnaround times, allowing you to receive a custom econometrics research paper even with urgent deadlines.
• Timely delivery : We prioritize timely delivery to ensure that you have sufficient time to review the research paper, request revisions if needed, and submit it within your academic timeline.
• 24/7 support : Our customer support team is available 24/7 to address any queries or concerns you may have. We are committed to providing prompt assistance and ensuring a smooth and satisfactory experience.
• Absolute privacy : We value your privacy and confidentiality. Your personal information and order details are treated with the utmost confidentiality and are never shared with third parties.
• Easy order tracking : Our user-friendly platform allows you to track the progress of your order, communicate with your assigned writer, and stay updated on the status of your research paper.
• Money-back guarantee : We strive for customer satisfaction, and if you are not fully satisfied with the delivered research paper, we offer a money-back guarantee, ensuring a risk-free experience.

By availing our writing services, you can save time, overcome challenges, and receive a custom econometrics research paper that meets your academic requirements. Place your order with iResearchNet and let our expert writers assist you in your pursuit of academic success.

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Life-cycle Forces make Monetary Policy Transmission Wealth-centric

This paper adds life-cycle features to a New Keynesian model and shows how this places financial wealth at the center of consumption/saving decisions, thereby enriching the determinants of aggregate demand and affecting the transmission of monetary policy. As retirement preoccupations strengthen, the potency of conventional monetary policy declines and depends more on the response of asset prices (supporting central banks closely monitoring the impact of monetary policy on asset prices). Especially “low/high for long” policies are shown to often have only muted effects on economic activity due to offsetting income and substitution effects of interest rates, in a way that can be compounded by Quantitative Easing. We also show why the presence of life-cycle forces can favor a monetary policy strategy which stabilizes asset prices in response to financial shocks. Being explicit about the role of retirement savings in aggregate demand therefore offers new perspectives on several aspects of monetary interventions.

We thank Ricardo Caballero, Ben Moll, Thijs Knaap, Jean-Paul L'Huillier, Tatiana Kirsanova, Alessandro Rebucci, audiences at the 2023 SED conference in Cartagena, the 5th WMMF Conference in Warsaw, the 2023 PSE Macro Days, the Reserve Bank of Australia, and the Bank of England for useful comments and discussions. The views expressed in this paper are those of the authors, and not necessarily those of the BIS, the Bank of England or its committees. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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What We Get Wrong about the Effects of Population Growth

New research co-authored by Professor Jason Dana finds that people over-focus on increased consumption without considering the positive effects of increased production.

• Jason Dana Associate Professor of Management and Marketing

When Yale SOM’s Jason Dana asks people to think about a consumer good that has gotten more affordable over time—say, televisions—they are confident they understand why: the underlying technology has improved and production is more efficient, so the product has come down in cost.

But when he asks them whether in general real costs—defined as the amount of work required to purchase individual items—have gone up or down over time, they almost always respond incorrectly, maintaining with just as much confidence that costs are on the rise. (Nominal prices, tied to inflation, are distinct from real costs and do generally rise over time.)

In new research, Dana, his former Yale SOM colleague George E. Newman, now of the University of Toronto; and Yale SOM PhD graduate Guy Voichek, a faculty member at Imperial College London, offer a label for this kind of thinking: “efficiency neglect.” Through several experiments, they show that, when thinking about population growth, people focus on increased demand, failing to consider the increased productivity that typically comes along with it. These beliefs are relatively benign at the individual level—our personal finance decisions don’t usually affect people outside our home or family —but can have downstream effects on society at large. For instance, Dana points out, people who don’t consider the benefits of population growth may support politicians who favor protectionist trade policies or limits on immigration.

“You can live your own individual life pretty well without understanding economics,” Dana explains. “But that lack of understanding probably does lead us to favor sometimes harmful or wasteful economic policies because the stuff that’s intuitive to us is not necessarily correct.”

The paper follows a bigger-picture analysis of lay economic reasoning that Dana and another coauthor published in January . In that research, Dana argues that non-economists’ understanding of markets and economic activity is understudied to the detriment of the consumer psychology field.

“Economists don’t tend to care very much, and psychologists tend not to know a lot about economics,” he says. “My work has always been kind of at the nexus of those two things.”

The belief that resources cannot keep up with demand—called depletionism—is not new. The authors point out that Plato predicted that a city-state might need to kick people out if its population grew past 5,040 households; in 1980, the biologist Paul Ehrlich famously lost a bet with the economist Julian Simon that the cost of several natural resources—including copper—would increase over the next decade.

“Because copper was used in plumbing, and all these people from developing countries were building houses, he thought, ‘Where is all this copper going to come from?’” Dana says. “But we can laugh at that now. We simply came up with a substitute.”

For the new paper, the researchers examined Americans’ views on costs over time and on how population growth would affect goods in a hypothetical thriving nation, and whether prompting people to think about production efficiencies mitigated their depletionist views, among other questions.

They found that prompting people to think about efficiency gains did counter depletionist thinking but not for everyone. When told to consider that more people can lead to more workers, more innovative ideas, and more incentives to improve technology, 27% of respondents thought goods in the hypothetical nation would be more abundant in the future, up from 6% without the prompt. The jump is significant, but future abundance is still a minority belief.

“If it were easy to shed these ideas, they would have been shed already,” Dana says.

Dana, who is currently studying people’s understanding of inflation, says he hopes the research will prompt further inquiries into lay economic beliefs, especially among marketing and consumer research scholars. As a Yale Center for Customer Insights blog post about his January paper points out, such research is “essential to understanding consumer psychology—why people act the way they do in market exchanges.” But it also may be essential to a more productive world, helping policymakers understand how to influence people’s understanding of bigger economic questions involving trade and immigration.

Dana finds the work fascinating.

“I really like talking about lay economics,” he says. “I know how economists think, and I am also acutely aware that people don’t think like economists.”

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Thinking of Pursuing a PhD in Economics? Info on Graduate School and Beyond

Kasey Chatterji-Len and Anna Kovner

Becoming a PhD economist can provide a fulfilling and financially secure career path. However, getting started in the field can be daunting if you don’t know much about the preparation you’ll need and the available job opportunities. If you’re wondering what it means to be an economics researcher or how to become one, please read on. We’ll review how to prepare for a career in economics research, what an economics PhD program entails, and what types of opportunities it might bring. Economic education is a core component of the Federal Reserve Bank of New York’s mission to serve the community. To empower would-be economists, this post provides information for students who seek a career in economics research. We hope this information will be helpful to students interested in economics, regardless of their background and economic situation.  This information is most applicable to students applying to programs in the United States.  

The Breadth of Economics Research  

Academic disciplines conduct research in different ways, so it’s important to have a basic understanding of the types of questions economists ask and how they approach answering them. There are many definitions of economics, but a broadly useful one is the study of how people, organizations, and governments make decisions under different constraints, and how those decisions may affect their outcomes. 

When answering these questions, economists seek to ground their analyses in models and to be quantitatively precise about the effects they assign to any given cause. The range of topics economists can study is wide, but the accepted approaches to answering questions are stricter. Some examples of what economists might ask: 

• How do different public housing programs affect the children who live there? 
• Does a certain type of law encourage businesses to innovate? 
• How will a change in the interest rate affect inflation and unemployment rates? 
• How much does affordable health insurance improve people’s health? 
• How can poor countries eradicate poverty? 

There are many different subfields within economics, including, but not limited to behavioral, econometrics, energy/environmental, development, financial, international, monetary, public, and urban economics. You can familiarize yourself with the latest work in economics by subscribing to working paper series, such as NBER’s New This Week or the New York Fed’s Staff Reports . To get an idea of the breadth of questions economists can answer, you could listen to Stephen Dubner’s “ Freakonomics Radio ” podcast. You may also want to explore the Journal of Economic Perspectives , the New York Fed’s Liberty Street Economics blog, VoxDev , or VoxEU .  

What Is a PhD Program Like?    

Economics PhD programs typically last five to seven years. Unlike masters programs, they are often fully funded with a stipend, though most require students to complete teaching assistant and/or research assistant (RA) work as part of their funding package. In the first two years, students take classes, many of which are mathematically demanding. The rest of the program can include additional classes but is primarily devoted to original research with the aim of producing publishable papers that will constitute the dissertation.  

Faculty advisors are a central part of PhD programs, as students look to them for guidance during the research process. Economics PhD programs are offered within university economics departments, but there are similar programs in public policy and business schools. You can look at their websites to understand any differences in coursework and subsequent job placements. 

What Can You Do with an Economics PhD?  

Upon graduation, students can obtain jobs in a variety of industries. Many PhD students hope to become university professors. Governments and public policy-related institutions such as the Federal Reserve System, the U.S. federal government, the World Bank, and the International Monetary Fund (IMF) also hire economists to work on policy, lead programs, and conduct research. Finally, economics PhD graduates can also find employment at a variety of private sector companies, including banks, economic consulting firms, and big tech companies. The pay for these different positions can vary. According to the American Economics Association (AEA), the average starting salary for economics assistant professors in 2022-23 was approximately $140,000 at PhD granting institutions and $98,000 at BA granting institutions. 

Programs often publish the placements of their PhD graduates, so you can look online to see specific employment outcomes. See, for example, the University of Maryland’s placements . Ultimately, economists are highly regarded as authorities on a variety of topics. Governments, nonprofits, philanthropic foundations, financial institutions, and non-financial businesses all look to economists to answer important questions about how to best achieve their goals. Thus, earning an economics Ph.D. can potentially help you to influence issues that are important to you. 

Preparing for an Economics PhD Program  

There are several components to an economics PhD program application: college transcripts, GRE scores, letters of recommendation, and personal statements. Please download the Appendix linked below to learn more about transcripts and letters of recommendation. The Appendix details ways in which you can select coursework, obtain research experience, and develop relationships to position yourself for success as a PhD applicant.  

If you feel that you are too far along in your academic career to take enough of the classes described in the Appendix, this does not necessarily preclude you from pursuing an economics PhD. For example, it’s possible to take some of these classes through a master’s program, or through a pre-doctoral RA job. Some pre-doctoral RA jobs, such as the one here at the New York Fed , may enable you to take classes in preparation for graduate school. If you are concerned about your transcript, reach out to an economist at your university for advice; program standards for coursework and grades vary, and it’s a good idea to get more personalized advice. 

Research Experience   

If you’re interested in becoming an economics researcher and applying to PhD programs, it’s best to get research experience as soon as possible. Working as an RA is a great way to learn how to conduct research and get a better idea of whether it’s the right career path for you. Additionally, it can help you obtain a letter of recommendation for graduate school applications and improve your qualifications.  

All types of academic research can be enriching, but it’s beneficial to gain experience working directly with an economist. To find a position, you can reach out to professors whose work you find interesting or find an RA program at your school. Typical RA tasks may involve data collection and cleaning, as well as running analyses and creating charts to represent results. This is where coding skills become crucial; having taken math, statistics, and econometrics courses will also enable you to take on more responsibilities. 

You may also have the opportunity to conduct your own research, possibly under the supervision of a professor at your university. This research could be self-initiated or part of a course such as a thesis workshop. Self-directed research is a great opportunity to learn about all stages of the research process. It’s also an excellent opportunity to create a writing sample for graduate school applications. Ultimately, though, your motivation for conducting your own research project should be that you want to answer a question.  One thing economists have in common is a love of answering questions using data and theory. 

Research experience is also often obtained after completing an undergraduate or master’s degree. Taking on a full-time RA position before applying to PhD programs is very common and can make you a more competitive applicant. You may either get an RA job working for a professor or participate in a pre-doctoral RA program.  

Research assistant programs are more structured than positions with individual professors or projects, which could be helpful. Universities, parts of the government, think tanks, research organizations, and the Federal Reserve System are all good places to look for research assistant programs. To help you decide which opportunities are most desirable, you may want to ask potential employers : Where do people in this program tend to go afterward? Will I be working directly with an economist? How much of my time will be spent on academic research work? Will I be able to take classes as part of this program? Considering whether an economist will be able to evaluate your performance is an important factor for recommendation letters. The ability to take classes, either through tuition reimbursement or waivers, can also be an important benefit. 

The Research Analyst program here at the Federal Reserve Bank of New York is one example of these programs and you should check it out here . The Federal Reserve Board of Governors also has a large program, and many other regional Federal Reserve Banks have similar programs. In addition, the PREDOC website and the  NBER post listings of RA opportunities. J-PAL and IPA also tend to recruit RAs for economic development projects. Another source of RA opportunities is the @econ_ra account on X. 

Who Should Get a PhD in Economics?  

A PhD may not be for everyone, but it is for anyone—people of all genders, religions, ethnicities, races, and national origins have PhDs in economics. Many economists majored in economics, but others majored in math, physics, or chemistry. Because economics is such an integral part of policymaking, it is important that economists come from a wide range of backgrounds so policy can be stronger and more effective. The inclusion of differing perspectives helps ensure that the contribution of economists to work in public policy, academia, and beyond effectively serves the broadest range of society. 

• Coursework Appendix

Kasey Chatterji-Len is a research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Anna Kovner  is the director of Financial Stability Policy Research in the Bank’s Research and Statistics Group.

How to cite this post: Kasey Chatterji-Len and Anna Kovner, “Thinking of Pursuing a PhD in Economics? Info on Graduate School and Beyond,” Federal Reserve Bank of New York Liberty Street Economics , May 31, 2024, https://libertystreeteconomics.newyorkfed.org/2024/05/thinking-of-pursuing-a-phd-in-economics-info-on-graduate-school-and-beyond/.

You may also be interested in: AEA: Resources for Students

PREDOC: Guidance for Undergraduates

RA Positions-Not at the NBER

Disclaimer The views expressed in this post are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author(s).

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Introduction to Financial Econometrics and Statistics

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The main purposes of this introduction chapter are (i) to discuss important financial econometrics and statistics which have been used in finance and accounting research and (ii) to present an overview of 98 chapters which have been included in this handbook. Sections  1.2 and 1.3 briefly review and discuss financial econometrics and statistics. Sections  1.4 and 1.5 discuss application of financial econometrics and statistics. Section  1.6 first classifies 98 chapters into 14 groups in accordance with subjects and topics. Then this section has classified the keywords from each chapter into two groups: finance and accounting topics and methodology topics. Overall, this chapter gives readers of this handbook guideline of how to apply this handbook to their research.

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Financial Panel Data Models, Strict Versus Contemporaneous Exogeneity, and Durbin-Wu-Hausman Specification Tests

Introduction

Aitchison, J., & Brown, J. A. C. (1973). Lognormal distribution , with special reference to its uses in economics . London: Cambridge University Press.

Google Scholar  

Ait-Sahalia, Y., & Lo, A. W. (2000). Nonparametric risk management and implied risk aversion. Journal of Econometrics, 94 , 9–51.

Article   Google Scholar  

Amemiya, T. (1974). A note on a fair and Jaffee model. Econometrica, 42 , 759–762.

Anderson, T. W. (1994). The statistical analysis of time series . New York: Wiley-Interscience.

Book   Google Scholar  

Anderson, T. W. (2003). An introduction to multivariate statistical analysis . New York: Wiley-Interscience.

Angrist, J. D., & Lavy, V. (1999). Using Maimonides’ rule to estimate the effect of class size on scholastic achievement. Quarterly Journal of Economics, 14 (2), 533–575.

Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68 (1), 29–51.

Bakshi, G., Cao, C., & Chen, Z. (1997). Empirical performance of alternative option pricing models. Journal of Finance, 52 (5), 2003–2049.

Baltagi, B. (2008). Econometric analysis of panel data (4th ed.). Chichester: Wiley.

Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81 (3), 637–654.

Black, F., Jensen, M. C., & Scholes, M. (1972). The capital asset pricing model: Some empirical tests. In M. C. Jensen (Ed.), Studies in the theory of capital markets . New York: Praeger.

Blume, M. E., & Friend, I. (1973). A new look at the capital asset pricing model. Journal of Finance, 28 , 19–33.

Brown, S. J., & Goetzmann, W. N. (1997). Mutual fund styles. Journal of Financial Economics, 43 (3), 373–399.

Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2006). Robust inference with multiway clustering (Working Paper).

Chacko, G., & Viceira, L. M. (2003). Spectral GMM estimation of continuous-time processes. Journal of Econometrics, 116 (1), 259–292.

Chang, C. F. (1999). Determinants of capital structure and management compensation: The partial least squares approach . Ph.D. dissertation, Rutgers University.

Chang, H. S., & Lee, C. F. (1977). Using pooled time-series and cross section data to test the firm and time effects in financial analysis. Journal of Financial and Quantitative Analysis, 12 , 457–471.

Chang, C., Lee, A. C., & Lee, C. F. (2009). Determinants of capital structure choice: A structural equation modeling approach. Quarterly Review of Economic and Finance, 49 (2), 197–213.

Chen, H. Y. (2011). Momentum strategies, dividend policy, and asset pricing test . Ph.D. dissertation, Rutgers University.

Chen, S. N., & Lee, C. F. (1981). The sampling relationship between Sharpe’s performance measure and its risk proxy: Sample size, investment horizon and market conditions. Management Science, 27 (6), 607–618.

Chen, K. H., & Shimerda, T. A. (1981). An empirical analysis of useful finance ratios. Financial Management, 10 (1), 51–60.

Chen, W. P., Chung, H., Lee, C. F., & Liao, W. L. (2007). Corporate governance and equity liquidity: Analysis of S&P transparency and disclosure rankings. Corporate Governance: An International Review, 15 (4), 644–660.

Cheng, D. C., & Lee, C. F. (1986). Power of alternative specification errors tests in identifying misspecified market models. The Quarterly Review of Economics and Business, 16 (3), 6–24.

Cheng, K. F., Chu, C. K., & Hwang, R. C. (2010). Predicting bankruptcy using the discrete-time semiparametric hazard model. Quantitative Finance, 10 , 1055–1066.

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28 , 591–605.

Core, J. E. (2000). The directors’ and officers’ insurance premium: An outside assessment of the quality of corporate governance. Journal of Law, Economics, and Organization, 16 (2), 449–477.

Cox, J. C., Ross, S. A., & Rubinstein, M. (1979). Option pricing: A simplified approach. Journal of Financial Economics, 7 (3), 229–263.

Fair, R. C., & Jaffee, D. M. (1972). Methods of estimation of market in disequilibrium. Econometrica, 40 , 497–514.

Fama, E. F. (1971). Parameter estimates for symmetric stable distributions. Journal of the American Statistical Association, 66 , 331–338.

Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81 , 607–636.

Fok, R. C. W., Lee, C. F., & Cheng, D. C. (1996). Alternative specifications and estimation methods for determining random beta coefficients: Comparison and extensions. Journal of Financial Studies, 4 (2), 61–88.

Frecka, T. J., & Lee, C. F. (1983). Generalized financial ratio adjustment processes and their implications. Journal of Accounting Research, 21 , 308–316.

Gordon, J., & Gordon, M. (1997). The fiinite horizon expected return model. The Financial Analyst Journal, 53 , 52–61.

Granger, C. W. J., & Newbold, P. (1973). Some comments on the evaluation of economic forecasts. Applied Economics, 5 (1), 35–47.

Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2 , 111–120.

Granger, C. W. J., & Ramanathan, R. (1984). Improved methods of combining forecasts. Journal of Forecasting, 3 , 197–204.

Hamilton, J. D. (1994). Time series analysis . Princeton: Princeton University Press.

Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50 (4), 1029–1054.

Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica, 64 (2), 413–430.

Hansen, B. E. (1997). Approximate asymptotic p-values for structural change tests. Journal of Business and Economic Statistics, 15 , 60–67.

Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93 , 345–368.

Hansen, B. E. (2000a). Sample splitting and threshold estimation. Econometrica, 68 (3), 575–603.

Hansen, B. E. (2000b). Testing for structural change in conditional models. Journal of Econometrics, 97 , 93–115.

Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. Review of Financial Studies, 6 (2), 327–343.

Hsiao, C. (2003). Analysis of panel data (2nd ed.). Cambridge MA: Cambridge University Press.

Hutchinson, J. M., Lo, A. W., & Poggio, T. (1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance, 49 (3), 851–889.

Hwang, R. C., Cheng, K. F., & Lee, J. C. (2007). A semiparametric method for predicting bankruptcy. Journal of Forecasting, 26 , 317–342.

Hwang, R. C., Wei, H. C., Lee, J. C., & Lee, C. F. (2008). On prediction of financial distress using the discrete-time survival model. Journal of Financial Studies, 16 , 99–129.

Hwang, R. C., Cheng, K. F., & Lee, C. F. (2009). On multiple-class prediction of issuer crediting ratings. Journal of Applied Stochastic Models in Business and Industry, 25 , 535–550.

Hwang, R. C., Chung, H., & Chu, C. K. (2010). Predicting issuer credit ratings using a semiparametric method. Journal of Empirical Finance, 17 (1), 120–137.

Ittner, C. D., Larcker, D. F., & Rajan, M. V. (1997). The choice of performance measures in annual bonus contracts. Accounting Review, 72 (2), 231–255.

Jones, J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29 , 193–228.

Kao, L. J., & Lee, C. F. (2012). Alternative method for determining industrial bond ratings: theory and empirical evidence. International Journal of Information Technology & Decision Making, 11 (6), 1215–1236.

Kau, J. B., & Lee, C. F. (1976). Functional form, density gradient and the price elasticity of demand for housing. Urban Studies, 13 (2), 181–192.

Kau, J. B., Lee, C. F., & Sirmans, C. F. (1986). Urban econometrics: Model developments and empirical results . Greenwich: JAI Press. Research in Urban Economics, Vol. 6.

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis (9th ed.). New York: Wiley-Interscience.

Kim, D. (1995). The errors in the variables problem in the cross-section of expected stock returns. Journal of Finance, 50 (5), 1605–1634.

Kim, D. (1997). A reexamination of firm size, book-to-market, and earnings price in the cross-section of expected stock returns. Journal of Financial and Quantitative Analysis, 32 (4), 463–489.

Kim, D. (2010). Issues related to the errors-in-variables problems in asset pricing tests. In Handbook of quantitative finance and risk management (Part V, Chap. 70, pp. 1091–1108).

Kuan, C. M., & Hornik, K. (1995). The generalized fluctuation test: A unifying view. Econometric Reviews, 14 , 135–161.

Lambert, R., & Larcker, D. (1987). An analysis of the use of accounting and market measures of performance in executive compensation contracts. Journal of Accounting Research, 25 , 85–125.

Lee, C. F. (1973). Errors-in-variables estimation procedures with applications to a capital asset pricing model . Ph.D. dissertation, The State University of New York at Buffalo.

Lee, C. F. (1976a). A note on the interdependent structure of security returns. Journal of Financial and Quantitative Analysis, 11 , 73–86.

Lee, C. F. (1976b). Functional form and the dividend effect of the electric utility industry. Journal of Finance, 31 (5), 1481–1486.

Lee, C. F. (1977a). Functional form, skewness effect and the risk-return relationship. Journal of Financial and Quantitative Analysis, 12 , 55–72.

Lee, C. F. (1977b). Performance measure, systematic risk and errors-in-variable estimation method. Journal of Economics and Business, 39 , 122–127.

Lee, A. (1996). Cost of capital and equity offerings in the insurance industry . Ph.D. dissertation, The University of Pennsylvania in Partial.

Lee, C. F., & Chen, H. Y. (2013). Alternative errors-in-variable models and their application in finance research (Working Paper). Rutgers University.

Lee, A. C., & Cummins, J. D. (1998). Alternative models for estimating the cost of capital for property/casualty insurers. Review of Quantitative Finance and Accounting, 10 (3), 235–267.

Lee, C. F., & Jen, F. C. (1978). Effects of measurement errors on systematic risk and performance measure of a portfolio. Journal of Financial and Quantitative Analysis, 13 (2), 299–312.

Lee, K. W., & Lee, C. F. (2012). Multiple directorships, corporate ownership, firm performance (Working Paper). Rutgers University.

Lee, C. F., & Wu, C. C. (1985). The impacts of kurtosis on risk stationarity: Some empirical evidence. Financial Review, 20 (4), 263–269.

Lee, C. F., & Zumwalt, J. K. (1981). Associations between alternative accounting profitability measures and security returns. Journal of Financial and Quantitative Analysis, 16 , 71–93.

Lee, C. F., Newbold, P., Finnerty, J. E., & Chu, C. C. (1986). On accounting-based, market-based and composite-based beta predictions: Method and implications. Financial Review, 21 , 51–68.

Lee, C. F., Wei, K. C. J., & Bubnys, E. L. (1989). The APT versus the multi-factor CAPM: Empirical evidence. Quarterly Review of Economics and Business, 29 , 6–16.

Lee, C. F., Wu, C. C., & Wei, K. C. J. (1990). Heterogeneous investment horizon and capital asset pricing model: Theory and implications. Journal of Financial and Quantitative Analysis, 25 , 361–376.

Lee, C. F., Tsai C. M., & Lee A. C. (2011a). Asset pricing with disequilibrium price adjustment: Theory and empirical evidence. Quantitative Finance .

Lee, C. F., Gupta, M. C., Chen, H. Y., & Lee, A. C. (2011b). Optimal payout ratio under uncertainty and the flexibility hypothesis: Theory and empirical evidence. Journal of Corporate Finance, 17 (3), 483–501.

Lee, C. F., Chien, C. C., Lin, H. C., Lin, Y. C., & Lin, F. C. (2013). Tradeoff between reputation concerns and economic dependence for auditors – Threshold regression approach (Working Paper). Rutgers University.

Leon, A., & Vaello-Sebastia, A. (2009). American GARCH employee stock option valuation. Journal of Banking and Finance, 33 (6), 1129–1143.

Lien, D. (2010). An note on the relationship between the variability of the hedge ratio and hedging performance. Journal of Futures Market, 30 (11), 1100–1104.

Lien, D., & Shrestha, K. (2007). An empirical analysis of the relationship between hedge ratio and hedging horizon using wavelet analysis. Journal of Futures Market, 27 (2), 127–150.

Liu, B. (2006). Two essays in financial econometrics: I: Functional forms and pricing of country funds. II: The term structure model of inflation risk premia . Ph.D. dissertation, Rutgers University.

Maddala, G. S., Rao, C. R., & Maddala, G. S. (1996). Handbook of statistics 14: Statistical methods in finance . Amsterdam: Elsevier Science & Technology.

Martin, C. (1990). Corporate borrowing and credit constrains: Structural disequilibrium estimates for the U.K. Review of Economics and Statistics, 72 (1), 78–86.

Mayer, W. J. (1989). Estimating disequilibrium models with limited a priori price-adjustment information. Journal of Econometrics, 41 (3), 303–320.

Miller, M. H., & Modigliani, F. (1966). Some estimates of the cost of capital to the utility industry, 1954-7. American Economic Review, 56 (3), 333–391.

Myers, R. J. (1991). Estimating time-varying optimal hedge ratios on futures markets. Journal of Futures Markets, 11 (1), 39–53.

Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55 (3), 703–708.

Ohlson, J. S. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 19 , 109–131.

Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22 , 435–480.

Pinches, G. E., & Mingo, K. A. (1973). A multivariate analysis of industrial bond ratings. Journal of Finance, 28 (1), 1–18.

Quandt, R. E. (1988). The econometrics of disequilibrium . New York: Basil Blackwell Inc.

Rendleman, R. J., Jr., & Barter, B. J. (1979). Two-state option pricing. Journal of Finance, 24 , 1093–1110.

Rubinstein, M. (1994). Implied binomial trees. Journal of Finance, 49 , 771–818.

Schroder, M. (1989). Computing the constant elasticity of variance option pricing formula. Journal of Finance, 44 (1), 211–219.

Sealey, C. W., Jr. (1979). Credit rationing in the commercial loan market: Estimates of a structural model under conditions of disequilibrium. Journal of Finance, 34 , 689–702.

Sears, R. S., & John Wei, K. C. (1988). The structure of skewness preferences in asset pricing model with higher moments: An empirical test. Financial Review, 23 (1), 25–38.

Shapiro, A. F. (2005). Fuzzy regression models (Working Paper). Penn State University.

Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74 , 101–124.

Thompson, S. B. (2011). Simple formulas for standard errors that cluster by both firm and time. Journal of Financial Economics, 99 (1), 1–10.

Thursby, J. G. (1985). The relationship among the specification error tests of Hausman, Ramsey and Chow. Journal of the American Statistical Association, 80 (392), 926–928.

Titman, S., & Wessels, R. (1988). The determinants of capital structure choice. Journal of Finance, 43 (1), 1–19.

Tsai, G. M. (2005). Alternative dynamic capital asset pricing models: Theories and empirical results . Ph.D. dissertation, Rutgers University.

Van Der Klaauw, W. (2002). Estimating the effect of financial aid offers on college enrollment: A regression-discontinuity approach. International Economic Review, 43 (4), 1249–1287.

Wei, K. C. (1984). The arbitrage pricing theory versus the generalized intertemporal capital asset pricing model: Theory and empirical evidence . Ph.D. dissertation, University of Illinois at Urbana-Champaign.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48 (4), 817–838.

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge, MA: The MIT Press.

Yang, C. C. (1989). The impact of new equity financing on firms’ investment, dividend and debt-financing decisions . Ph.D. dissertation, The University of Illinois at Urbana-Champaign.

Yang, C. C., Lee, C. F., Gu, Y. X., & Lee, Y. W. (2010). Co-determination of capital structure and stock returns – A LISREL approach: An empirical test of Taiwan stock markets. Quarterly Review of Economic and Finance, 50 (2), 222–233.

Zeileis, A., Leisc, F., Hornik, K., & Kleiber, C. (2002). Strucchange: An R package for testing for structural change in linear regression models. Journal of Statistical Software, 7 , 1–38.

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Appendix 1: Brief Abstracts and Keywords for Chapters 2 to 99

1.1.1 chapter 2: experience, information asymmetry, and rational forecast bias.

This chapter uses a Bayesian model of updating forecasts in which the bias in forecast endogenously determines how the forecaster’s own estimates weigh into the posterior beliefs. The model used in this chapter predicts a concave relationship between accuracy in forecast and posterior weight that is put on the forecaster’s self-assessment. This chapter then uses a panel regression to test the analytical findings and find that an analyst’s experience is indeed concavely related to the forecast error.

Keywords: Financial analysts, Forecast accuracy, Information asymmetry, Forecast bias, Bayesian updating, Panel regressions, Rational bias, Optional bias, Analyst estimation, Analyst experience

1.1.2 Chapter 3: An Overview of Modeling Dimensions for Performance Appraisal of Global Mutual Funds (Mutual Funds)

This paper examines various performance models derived by financial experts across the globe. A number of studies have been conducted to examine investment performance of mutual funds of the developed capital markets. The measure of performance of financial instruments is basically dependent on three important models derived independently by Sharpe, Jensen, and Treynor. All three models are based on the assumptions that (1) all investors are averse to risk, and are single period expected utility of terminal wealth maximizers, (2) all investors have identical decision horizons and homogeneous expectations regarding investment opportunities, (3) all investors are able to choose among portfolios solely on the basis of expected returns and variance of returns, (4) all trans-actions costs and taxes are zero, and (5) all assets are infinitely divisible. Overall, this paper has examined nine alternative mutual funds measure. The method used in this kind of research is regression analysis.

Keywords : Financial modeling, Mutual funds, Performance appraisal, Global investments, Evaluation of funds, Portfolio management, Systematic risk, Unsystematic risk, Risk adjusted performance, Prediction of price movements

1.1.3 Chapter 4: Simulation as a Research Tool for Market Architects

This chapter uses simulation to gain insights into trading and market structure topic by two statistical methods. The statistical methods we use include experimental design, and careful controls over experimental parameters such as the instructions given to participants. The first is discrete event simulation and the model of computer-generated trade order flow that we describe in Sect.  3 . To create a realistic, but not ad hoc, market background, we use draws from a log-normal returns distribution to simulate changes in a stock’s fundamental value, or P*. The model uses price-dependent Poisson distributions to generate a realistic flow of computer-generated buy and sell orders whose intensity and supply-demand balance vary over time. The order flow fluctuations depend on the difference between the current market price and the P* value. In Sect.  4 , we illustrate the second method, which is experimental control to create groupings of participants in our simulations that have the same trading “assignment.” The result is the ability to make valid comparisons of trader’s performance in the simulations.

Keywords : Trading simulations, Market microstructure, Order flow models, Random walk models, Experimental economics, Experimental control

1.1.4 Chapter 5: Motivations for Issuing Putable Debt: An Empirical Analysis

This paper is the first to examine the motivations for issuing putable bonds in which the embedded put option is not contingent upon a company-related event. We find that the market favorably views the issue announcement of these bonds that we refer to as bonds with European put options or European putable bonds. This response is in contrast to the response documented by the literature to other bond issues (straight, convertible, and most studies examining poison puts), and to the response documented in the current paper to the issue announcements of poison-put bonds. Our results suggest that the market views issuing European putable bonds as helping mitigate security mispricing. Our study is an application of important statistical methods in corporate finance, namely, Event Studies and the use of General Method of Moments for cross-sectional regressions.

Keywords : Agency costs, Asymmetric information, Corporate finance, Capital structure, Event study methodology, European put, General method of moments, Management myopia, Management entrenchment, Poison put

1.1.5 Chapter 6: Multi Risk-Premia Model of U.S. Bank Returns: An Integration of CAPM and APT

Interest rate sensitivity of bank stock returns has been studied using an augmented CAPM: a multiple regression model with market returns and interest rate as independent variables. In this chapter, we test an asset-pricing model in which the CAPM is augmented by three orthogonal factors which are proxies for the innovations in inflation, maturity risk, and default risk. The methodologies used in this chapter are multiple regression and factor analysis.

Keywords : CAPM, APT, Bank stock return, Interest rate risk, Orthogonal factors, Multiple regression

1.1.6 Chapter 7: Non-parametric Bounds for European Option Prices

This chapter derives a new nonparametric lower bound and provides an alternative interpretation of Ritchken’s (1985) upper bound to the price of the European option. In a series of numerical examples, our new lower bound is substantially tighter than previous lower bounds. This is prevalent especially for out-of-the-money (OTM) options where the previous lower bounds perform badly. Moreover, we present that our bounds can be derived from histograms which are completely nonparametric in an empirical study. We first construct histograms from realizations of S&P 500 index returns following Chen et al. (2006), calculate the dollar beta of the option and expected payoffs of the index and the option, and eventually obtain our bounds. We discover violations in our lower bound and show that those violations present arbitrage profits. In particular, our empirical results show that out-of-the-money calls are substantially overpriced (violate the lower bound). The methodologies used in this chapter are nonparametric, option pricing model, and histograms methods.

Keywords : Option bounds, Nonparametric, Black-Scholes model, European option, S&P 500 index, Arbitrage, Distribution of underlying asset, Lower bound, Out-of-the-money, Kernel pricing

1.1.7 Chapter 8: Can Time-Varying Copulas Improve Mean-Variance Portfolio?

This chapter evaluates whether constructing a portfolio using time-varying copulas yields superior returns under various weight updating strategies. Specifically, minimum-risk portfolios are constructed based on various copulas and the Pearson correlation, and a 250-day rolling window technique is adopted to derive a sequence of time-varied dependences for each dependence model. Using daily data of the G-7 countries, our empirical findings suggest that portfolios using time-varying copulas, particularly the Clayton-dependence, outperform those constructed using Pearson correlations. The above results still hold under different weight updating strategies and portfolio rebalancing frequencies. The methodologies used in this chapter are Copulas, GARCH, Student’s t -Copula, Gumbel Copula, Clayton Copula, Time-Varying Dependence, Portfolio Optimization, and Bootstrap.

Keywords : Copulas, Time-varying dependence, Portfolio optimization, Bootstrap, Out-of-sample return, Performance evaluation, GARCH, Gaussian copula, Student’s t -copula, Gumbel copula, Clayton copula

1.1.8 Chapter 9: Determinations of Corporate Earnings Forecast Accuracy: Taiwan Market Experience

This chapter examines the accuracy of the earnings forecasts by the following test methodologies. Multiple Regression Models are used to examine the effect of six factors: firm size, market volatility, trading volume turnover, corporate earnings variances, type of industry, and experience. If the two-sample groups are related, Wilcoxon Two-Sample Test will be used to determine the relative earnings forecast accuracy. Readers are well advised and referred to the chapter appendix for methodological issues such as sample selection, variable definition, regression model, and Wilcoxon tow-sample test.

Keywords : Multiple regression, Wilcoxon two-sample test, Corporate earnings, Forecast accuracy, Management earnings, Firm size, Corporation regulation, Volatility, Trade turnover, Industry

1.1.9 Chapter 10: Market-Based Accounting Research (MBAR) Models: A Test of ARIMAX Modeling

This study uses standard models such as earnings level and earnings changes, among others. Models that fit better to the data drawn from companies listed on the Athens Stock Exchange have been selected employing autoregressive integrated moving average with exogenous variables (ARIMAX) models. Models I (price on earnings model) “II (returns on change in earnings divided by beginning-of-period price and prior period)” V (returns on change in earnings over opening market value), VII (returns deflated by lag of 2 years on earnings over opening market value), and IX (differenced-price model) have statistically significant coefficients of explanatory variables. These models take place with backward looking information instead of forward looking information that recent literature is assessed. The methodologies used in this chapter are price on earnings model, return models, autoregressive moving average with exogenous variables (ARIMAX), minimum value of squared residuals (MSE loss function), and Dickey-Fuller test.

Keywords : Market-based accounting research (MBAR), Price on earnings model, Earnings level, Earnings change, Return models, Autoregressive moving average with exogenous variables (ARIMAX), Minimum value of squared residuals (MSE loss function), Unit root tests stationarity, Dickey-Fuller test

1.1.10 Chapter 11: An Assessment of Copula Functions Approach in Conjunction with Factor Model in Portfolio Credit Risk Management

This study uses a mixture of the dynamic factor model of Duffee (1999) and a contagious effect in the specification of a Hawkes process, a class of counting processes which allows intensities to depend on the timing of previous events (Hawkes 1971). Using the mixture factor- contagious-effect model, Monte Carlo simulation is performed to generate default times of two hypothesized firms. The goodness-of-fit of the joint distributions based on the most often used copula functions in literature, including the Normal, t-, Clayton, Frank, and Gumbel copula, respectively, is assessed against the simulated default times. It is demonstrated that as the contagious effect increases, the goodness-of-fit of the joint distribution functions based on copula functions decreases, which highlights the deficiency of the copula function approach.

Keywords : Static factor model, Dynamic factor model, Correlated defaults,Contagious effect, Hawkes process, Monte Carlo simulation, Normal copula, t -copula, Clayton copula, Frank copula, Gumbel copula

1.1.11 Chapter 12: Assessing Importance of Time-Series Versus Cross-Sectional Changes in Panel Data: A Study of International Variations in Ex-Ante Equity Premia and Financial Architecture

This chapter uses simultaneous equation modeling and uses Hausman test to determine whether to report fixed or random-effects estimates. We first report random-effects estimates based on the estimation procedure of Baltagi (Baltagi 1981; Baltagi and Li 1995; Baltagi and Li 1994). We consider that the error component two-stage least squares (EC2SLS) estimator of Baltagi and Li (1995) is more efficient than the generalized two-stage least squares (G2SLS) estimator of Balestra and Varadharajan-Krishnakumar (1987). For our second estimation procedure, for comparative purposes, we use the dynamic panel modeling estimates recommended by Blundell and Bond (1998). We employ the model of Blundell and Bond (1998), as these authors argue that their estimator is more appropriate than the Arellano and Bond (1991) model for smaller time periods relative to the size of the panels. We also use this two-step procedure, use as an independent variable the first lag of the dependent variable, reporting robust standard errors of Windmeijer (2005). Thus, our two different panel estimation techniques place differing emphasis on cross-sectional and time-series effects, with the Baltagi-Li estimator emphasizing cross-sectional effects and the Blundell-Bond estimator emphasizing time-series effects.

Keywords: Panel data estimates, Time-series and cross-sectional effects, Econometrics, Financial institutions, Banks, Financial markets, Comparative financial systems, Legal traditions, Uncertainty avoidance, Trust, Property rights, Error component two-stage least squares (EC2SLS), The generalized two-stage least squares (G2SLS)

1.1.12 Chapter 13: Does Banking Capital Reduce Risk?: An Application of Stochastic Frontier Analysis and GMM Approach

This chapter employs stochastic frontier analysis to create a new type of instrumental variable. The unrestricted frontier model determines the highest possible profitability based solely on the book value of assets employed. We develop a second frontier based on the level of bank holding company capital as well as the amount of assets. The implication of using the unrestricted model is that we are measuring the unconditional inefficiency of the banking organization. This chapter applies generalized method of moments (GMM) regression to avoid the problem caused by departure from normality. To control for the impact of size on a bank’s risk-taking behavior, the book value of assets is considered in the model. The relationship between the variables specifying bank behavior and the use of equity is analyzed by GMM regression.

Keywords : Bank capital, Generalized method of moments, Stochastic frontier analysis, Bank risks, Bank holding companies, Endogeneity of variables

1.1.13 Chapter 14: Evaluating Long-Horizon Event Study Methodology

This chapter examines the performance of more than 20 different testing procedures that fall into two categories. First, the buy-and-hold benchmark approach uses a benchmark to measure the abnormal buy-and-hold return for every event firm, and tests the null hypothesis that the average abnormal return is zero. Second, the calendar-time portfolio approach forms a portfolio in each calendar month consisting of firms that have had an event within a certain time period prior to the month, and tests the null hypothesis that the intercept is zero in the regression of monthly portfolio returns against the factors in an asset-pricing model. This chapter also evaluates the performance of bootstrapped Johnson’s skewness-adjusted t -test. This computation-intensive procedure is considered because the distribution of long-horizon abnormal returns tends to be highly skewed to the right. The bootstrapping method uses repeated random sampling to measure the significance of relevant test statistics. Due to the nature of random sampling, the resultant measurement of significance varies each time such a procedure is used. We also evaluate simple nonparametric tests, such as the Wilcoxon signed-rank test or the Fisher’s sign test, which are free from random sampling variation.

Keywords : Long-horizon event study, Johnson’s Skewness-adjusted t -test, Weighted least-squares regression, Bootstrap test, Calendar-time portfolio approach, Fama-French three-factor model, Johnson’s skewness-adjusted t-statistic, Large-scale simulations

1.1.14 Chapter 15: Effect of Unexpected Volatility Shocks on Intertemporal Risk-Return Relation

This chapter employs the ANST-GARCH model that is capable of capturing the asymmetric volatility effect of a positive and negative return shock. The key feature of the model is the regime-shift mechanism that allows a smooth, flexible transition of the conditional volatility between different states of volatility persistence. The regime-switching mechanism is governed by a logistic transition function that changes values depending on the level of the previous return shock. With a negative (positive) return shock, the conditional variance process is described as a high (low)-persistence-in-volatility regime. The ANST-GARCH model describes the heteroskedastic return dynamics more accurately and generates better volatility forecasts.

Keywords : Intertemporal risk-return relation, Unexpected volatility shocks, Time-varying rational expectation hypothesis, Stock market overreaction, Expected market risk premium, Volatility feedback effect, Asymmetric mean reversion, Asymmetric volatility response, Time-varying volatility, Volatility regime switching, ANST-GARCH model

1.1.15 Chapter 16: Combinatorial Methods for Constructing Credit Risk Ratings

This chapter uses a novel method, the Logical Analysis of Data (LAD), to reverse-engineer and construct credit risk ratings which represent the creditworthiness of financial institutions and countries. LAD is a data-mining method based on combinatorics, optimization, and Boolean logic that utilizes combinatorial search techniques to discover various combinations of attribute values that are characteristic of the positive or negative character of observations. The proposed methodology is applicable in the general case of inferring an objective rating system from archival data, given that the rated objects are characterized by vectors of attributes taking numerical or ordinal values. The proposed approaches are shown to generate transparent, consistent, self-contained, and predictive credit risk rating models, closely approximating the risk ratings provided by some of the major rating agencies. The scope of applicability of the proposed method extends beyond the rating problems discussed in this study, and can be used in many other contexts where ratings are relevant. This study also uses multiple linear regression to derive the logical rating scores.

Keywords : Credit risk rating, Reverse-engineering, Logical analysis of data, Combinatorial optimization, Data-mining, Creditworthiness, Financial strength, Internal rating, Preorder, Logical rating score

1.1.16 Chapter 17: Dynamic Interactions in the Taiwan Stock Exchange: A Threshold VAR Models

This chapter constructs a six-variable VAR model (including NASDAQ returns, TSE returns, NT/USD returns, net foreign purchases, net domestic investment companies (dic) purchases, and net registered trading firms (rtf) purchases) to examine: (i) the interaction among three types of institutional investors, particularly to test whether net foreign purchases lead net domestic purchases by dic and rtf (the so-called demonstration effect); (ii) whether net institutional purchases lead market returns or vice versa, and (iii) whether the corresponding lead-lag relationship is positive or negative? Readers are well advised to refer to chapter appendix for detailed discussion of the unrestricted VAR model, the structural VAR model, and the threshold VAR analysis. The methodologies used in this chapter are multivariate threshold autoregression model, structural VAR, and Block Granger Causality.

Keywords : Demonstration effect, Multivariate threshold autoregression model, Foreign investment, Lead-lag relationship, Structural VAR, Block Granger causality, Institutional investors, Domestic investment companies, Registered trading firms, Qualified foreign institutional investors

1.1.17 Chapter 18: Methods of Denoising Financial Data

This chapter uses denoising analysis which imposes new challenges for financial data mining due to the irregularities and roughness observed in financial data, particularly, for instantaneously collected massive amounts of tick-by-tick data from financial markets for information analysis and knowledge extraction. Inefficient decomposition of the systematic pattern (the trend) and noises of financial data will lead to erroneous conclusions since irregularities and roughness of the financial data make the application of traditional methods difficult. The methodologies used in this chapter are linear filters, nonlinear filters, time-series analysis, trend extraction, and wavelet.

Keywords : Jump detection, Linear filters, Nonlinear filters, Time-series analysis, Trend extraction, Wavelet

1.1.18 Chapter 19: Analysis of Financial Time: Series Using Wavelet Methods

This chapter presents a set of tools, which allow gathering information about the frequency components of a time-series. In the first step, we discuss spectral analysis and filtering methods. Spectral analysis can be used to identify and to quantify the different frequency components of a data series. Filters permit to capture specific components (e.g. trends, cycles, seasonalities) of the original time-series. In the second step, we introduce wavelets, which are relatively new tools in economics and finance. They take their roots from filtering methods and Fourier analysis, but overcome most of the limitations of these two methods. Their principal advantages derive from: (i) combined information from both time-domain and frequency-domain and (ii) their flexibility as they do not make strong assumptions concerning the data generating process for the series under investigation.

Keywords : Filtering methods, Spectral analysis, Fourier transform, Wavelet filter, Continuous wavelet transform, Discrete wavelet transform, Multi-resolution analysis, Scale-by-scale decomposition, Analysis of variance, Case-Shiller home price indices

1.1.19 Chapter 20: Composite Goodness-of-Fit Tests for Left Truncated Loss Sample

This chapter derives the exact formulae for several goodness-of-fit statistics that should be applied to loss models with left-truncated data where the fit of a distribution in the right tail of the distribution is of central importance. We apply the proposed tests to real financial losses, using a variety of distributions fitted to operational loss and the natural catastrophe insurance claims data. The methodologies discussed in this chapter are goodness-of-fit tests, loss distribution, ruin probability, value-at-risk, Anderson-Darling statistic, Kolmogorov-Smirnov statistic.

Keywords : Goodness-of-fit tests, Left-truncated data, Minimum recording threshold, Loss distribution, Heavy-tailed data, Operational risk, Insurance, Ruin probability, Value-at-risk, Anderson-Darling statistic, Kolmogorov-Smirnov statistic

1.1.20 Chapter 21: Effect of Merger on the Credit Rating and Performance of Taiwan Security Firms

This chapter identifies and defines variables for merger synergy analysis followed by principal component factor analysis, variability percentage adjustment, and performance score calculation. Finally, Wilcoxon sign rank test is used for hypothesis testing. We extract principle component factors from a set of financial ratios. Percentage of variability explained and factor loadings are adjusted to get a modified average weight for each financial ratio. This weight is multiplied by the standardized Z value of the variable, and summed a set of variables get a firm’s performance score. Performance scores are used to rank the firm. Statistical significance of difference in pre- and post-merger rank is tested using the Wilcoxon sign rank.

Keywords: Corporate merger, Financial ratios, Synergy, Economies of scale, Credit rating, Variability percentage adjustment, Principle component factors, Firm’s performance score, Standardized Z, Wilcoxon rank test

1.1.21 Chapter 22: On-/Off-the-Run Yield Spread Puzzle: Evidence from Chinese Treasury Markets

This chapter uses on-/off-the-run yield spread to describe “on-/off-the-run yield spread puzzle” in Chinese treasury markets. To explain this puzzle, we introduce a latent factor in the pricing of Chinese off-the-run government bonds and use this factor to model the yield difference between Chinese on-the-run and off-the-run issues. We use the nonlinear Kalman filter approach to estimate the model. The methodologies used in this chapter are CIR model, nonlinear Kalman filter and Quasi-maximum likelihood model.

Keywords : On-/off-the-run yield spread, Liquidity, Disposition effect, CIR model, Nonlinear Kalman filter, Quasi-maximum likelihood

1.1.22 Chapter 23: Factor Copula for Defaultable Basket Credit Derivatives

This chapter uses a factor copula approach to evaluate basket credit derivatives with issuer default risk and demonstrate its application in a basket credit linked note (BCLN). We generate the correlated Gaussian random numbers by using the Cholesky decomposition, and then, the correlated default times can be decided by these random numbers and the reduced-form model. Finally, the fair BCLN coupon rate is obtained by the Monte Carlo simulation. We also discuss the effect of issuer default risk on BCLN. We show that the effect of issuer default risk cannot be accounted for thoroughly by considering the issuer as a new reference entity in the widely used one factor copula model, in which constant default correlation is often assumed. A different default correlation between the issuer and the reference entities affects the coupon rate greatly and must be taken into account in the pricing model.

Keywords : Factor copula, Issuer default, Default correlation, Reduced-form model, Basket credit derivatives, Cholesky decomposition, Monte Carlo simulation

1.1.23 Chapter 24: Panel Data Analysis and Bootstrapping: Application to China Mutual Funds

This chapter estimates double- and single-clustered standard errors by wild-cluster bootstrap procedure. To obtain the wild bootstrap samples in each cluster, we reuse the regressors (X), but modify the residuals by transforming the OLS residuals with weights which follow the popular two-point distribution suggested by Mammen (1993) and others. We then compare them with other estimates in a set of asset-pricing regressions. The comparison indicates that bootstrapped standard errors from double clustering outperform those from single clustering. They also suggest that bootstrapped critical values are preferred to standard asymptotic t -test critical values to avoid misleading test results.

Keywords : Asset-pricing regression, Bootstrapped critical values, Cluster standard errors, Double clustering, Firm and time effects, Finance panel data, Single clustering, Wild-cluster bootstrap

1.1.24 Chapter 25: Market Segmentation and Pricing of Closed-End Country Funds: An Empirical Analysis

This chapter finds that for closed-end country funds, the international CAPM can be rejected for the underlying securities (NAVs) but not for the share prices. This finding indicates that country fund share prices are determined globally, whereas the NAVs reflect both global and local prices of risk. Cross-sectional variations in the discounts or premiums for country funds are explained by the differences in the risk exposures of the share prices and the NAVs. Finally, this chapter shows that the share price and NAV returns exhibit predictable variation, and country fund premiums vary over time due to time-varying risk premiums. The chapter employs Generalized Method of Moments (GMM) to estimate stochastic discount factors and examines if the price of risk of closed-end country fund shares and NAVs is identical.

Keywords : Capital markets, Country funds, CAPM, Closed-end funds, Market segmentation, GMM, Net asset value, Stochastic discount factors, Time-varying risk, International asset pricing

1.1.25 Chapter 26: A Comparison of Portfolios Using Different Risk Measurements

This study uses three different risk measurements: the Mean-variance model, the Mean Absolute Deviation model, and the Downside Risk model. Meanwhile short selling is also taken into account since it is an important strategy that can bring a portfolio much closer to the efficient frontier by improving a portfolio’s risk-return trade-off. Therefore, six portfolio rebalancing models, including the MV model, MAD model and the Downside Risk model, with/without short selling, are compared to determine which is the most efficient. All models simultaneously consider the criteria of return and risk measurement. Meanwhile, when short selling is allowed, models also consider minimizing the proportion of short selling. Therefore, multiple objective programming is employed to transform multiple objectives into a single objective in order to obtain a compromising solution. An example is used to perform simulation, and the results indicate that the MAD model, incorporated with a short selling model, has the highest market value and lowest risk.

Keywords : Portfolio selection, Risk measurement, Short selling, MV model, MAD model, Downside risk model, Multiple objective programming, Rebalancing model, Value-at-risk, Conditional value-at-risk

1.1.26 Chapter 27: Using Alternative Models and a Combining Technique in Credit Rating Forecasting: An Empirical Study

This chapter first utilizes the ordered logit and the ordered probit models. Then, we use ordered logit combining method to weight different techniques’ probability measures, as described in Kamstra and Kennedy (1998) to form the combining model. The samples consist of firms in the TSE and the OTC market, and are divided into three industries for analysis. We consider financial variables, market variables as well as macroeconomic variables and estimate their parameters for out-of-sample tests. By means of Cumulative Accuracy Profile, the Receiver Operating Characteristics, and McFadden, we measure the goodness-of-fit and the accuracy of each prediction model. The performance evaluations are conducted to compare the forecasting results, and we find that combing technique does improve the predictive power.

Keywords : Bankruptcy prediction, Combining forecast, Credit rating, Credit risk, Credit risk index, Forecasting models, Logit regression, Ordered logit, Ordered probit, Probability density function

1.1.27 Chapter 28: Can We Use the CAPM as an Investment Strategy?: An Intuitive CAPM and Efficiency Test

The aim of this chapter is to check whether certain playing rules, based on the undervaluation concept arising from the CAPM, could be useful as investment strategies, and can therefore be used to beat the Market. If such strategies work, we will be provided with a useful tool for investors, and, otherwise, we will obtain a test whose results will be connected with the efficient Market hypothesis (EMH) and with the CAPM. The methodology used is both intuitive and rigorous: analyzing how many times we beat the Market with different strategies, in order to check whether when we beat the Market, this happens by chance.

Keywords : ANOVA, Approximately normal distribution, Binomial distribution, CAPM, Contingency tables, Market efficiency, Nonparametric tests, Performance measures

1.1.28 Chapter 29: Group Decision Making Tools for Managerial Accounting and Finance Applications

This chapter adopts an Analytic Hierarchy Process (AHP) approach to solve various accounting or finance problems such as developing a business performance evaluation system and developing a banking performance evaluation system. AHP uses hierarchical schema to incorporate nonfinancial and external performance measures. Our model has a broader set of measures that can examine external and nonfinancial performance as well as internal and financial performance. While AHP is one of the most popular multiple goals decision-making tools, Multiple Criteria and Multiple Constraint (MC 2 ) Linear Programming approach also can be used to solve group decision-making problems such as transfer pricing and capital budgeting problems. The methodologies used in this chapter are Analytic Hierarchy Process, multiple criteria and multiple constraint linear programming, and balanced scorecard and business performance evaluation.

Keywords : Analytic hierarchy process, Multiple criteria and multiple constraint linear programming, Business performance evaluation, Activity-based costing system, Group decision making, Optimal trade-offs, Balanced scorecard, Transfer pricing, Capital budgeting

1.1.29 Chapter 30: Statistics Methods Applied in Employee Stock Options

This study provides model-based and compensation-based approaches to price subjective value of employee stock options (ESOs). In model-based approach, we consider a utility-maximizing model that the employee allocates his wealth among the company stock, market portfolio, and risk-free bond, and then derive the ESO formulae which take into account illiquidity and sentiment effects. By using the method of change of measure, the derived formulae are simply like that of the market values with altered parameters. To calculate compensation-based subjective value, we group employees by hierarchical clustering with K-Means approach and back out the option value in an equilibrium competitive employment market. Further, we test illiquidity and sentiment effects on ESO values by running the regressions which consider the problem of standard errors in finance panel data.

Keywords : Employee stock option, Sentiment, Subjective value, Illiquidity, Change of measure, Hierarchical clustering with K-Means approach, Standard errors in finance panel data, Exercise boundary, Jump diffusion model

1.1.30 Chapter 31: Structural Change and Monitoring Tests

This chapter focuses on various structural change and monitoring tests for a class of widely used time-series models in economics and finance, including I ( 0 ), I ( 1 ), I ( d ) processes and the co-integration relationship. In general, structural change tests can be categorized into two types: One is the classical approach to testing for structural change, which employs retrospective tests using a historical data set of a given length; the other one is the fluctuation-type test in a monitoring scheme, which means for given a history period for which a regression relationship is known to be stable, we then test whether incoming data are consistent with the previously established relationship. Several structural changes such as CUSUM squared tests, the QLR test, the prediction test, the multiple break test, bubble tests, co-integration breakdown tests, and the monitoring fluctuation test are discussed in this chapter, and we further illustrate all details and usefulness of these tests.

Keywords : Co-integration breakdown test, Structural break, Long memory process, Monitoring fluctuation test, Boundary function, CUSUM squared test, Prediction test, Bubble test, Unit root time series, Persistent change

1.1.31 Chapter 32: Consequences of Option Pricing of a Long Memory in Volatility

This chapter use conditionally heteroskedastic time-series models to describe the volatility of stock index returns. Volatility has a long memory property in the most general models and then the autocorrelations of volatility decay at a hyperbolic rate; contrasts are made with popular, short memory specifications whose autocorrelations decay more rapidly at a geometric rate. Options are valued for ARCH volatility models by calculating the discounted expectations of option payoffs for an appropriate risk-neutral measure. Monte Carlo methods provide the expectations. The speed and accuracy of the calculations is enhanced by two variance reduction methods, which use antithetic and control variables. The economic consequences of a long memory assumption about volatility are documented, by comparing implied volatilities for option prices obtained from short and long memory volatility processes.

Keywords : ARCH models, Implied volatility, Index options, Likelihood maximization, Long memory, Monte Carlo, Option prices, Risk-neutral pricing, Smile shapes, Term structure, Variance reduction methods

1.1.32 Chapter 33: Seasonal Aspects of Australian Electricity Market

This chapter develops econometric models for seasonal patterns in both price returns and proportional changes in demand for Australian electricity. Australian Electricity spot prices differ considerably from equity spot prices in that they contain an extremely rapid mean reversion process. The electricity spot price could increase to a market cap price of AU$12,500 per Megawatt Hour (MWh) and revert back to a mean level (AUD$30) within a half hour interval. This has implications for derivative pricing and risk management. We also model extreme spikes in the data. Our study identifies both seasonality effects and dramatic price reversals in the Australian electricity market. The pricing seasonality effects include time-of-day, day-of-week, monthly, and yearly effects. There is also evidence of seasonality in demand for electricity.

Keywords : Electricity, Spot price, Seasonality, Outlier, Demand, Econometric modeling

1.1.33 Chapter 34: Pricing Commercial Timberland Returns in the United States

This chapter uses both parametric and nonparametric approaches to evaluate private- and public-equity timberland investments in the United States. Private-equity timberland returns are proxied by the NCREIF Timberland Index, whereas public-equity timberland returns are proxied by the value-weighted returns on a dynamic portfolio of the US publicly traded forestry firms that had or have been managing timberlands. Static estimations of the capital asset-pricing model and Fama-French three-factor model are obtained by ordinary least squares, whereas dynamic estimations are obtained by state-space specifications with the Kalman filter. In estimating the stochastic discount factors, linear programming is used.

Keywords : Alternative asset class, Asset pricing, Evaluation, Fama-French three-factor model, Nonparametric analysis, State-space model, Stochastic discount factor, Timberland investments, Time series, Time-varying parameter

1.1.34 Chapter 35: Optimal Orthogonal Portfolios with Conditioning Information

This chapter derives and characterizes optimal orthogonal portfolios in the presence of conditioning information in the form of a set of lagged instruments. In this setting, studied by Hansen and Richard (1987), the conditioning information is used to optimize with respect to the unconditional moments. We present an empirical illustration of the properties of the optimal orthogonal portfolios. The methodology in this chapter includes regression and maximum likelihood parameter estimation, as well as method of moments estimation. We form maximum likelihood estimates of nonlinear functions as the functions evaluated at the maximum likelihood parameter estimates.

Keywords : Asset-pricing tests, Conditioning information, Minimum variance efficiency, Optimal portfolios, Predicting returns, Portfolio management, Stochastic discount factors, Generalized, Method of moments, Maximum likelihood, Parametric bootstrap, Sharpe ratios

1.1.35 Chapter 36: Multi-factor, Multi-indicator Approach to Asset Pricing: Method and Empirical Evidence

This chapter uses a multifactor, multi-indicator approach to test the capital asset-pricing model (CAPM) and the arbitrage pricing theory (APT). This approach is able to solve the measuring problem in the market portfolio in testing CAPM, and it is also able to directly test APT by linking the common factors to the macroeconomic indicators. We propose a MIMIC approach to test CAPM and APT. The beta estimated from the MIMIC model by allowing measurement error on the market portfolio does not significantly improve the OLS beta, while the MLE estimator does a better job than the OLS and GLS estimators in the cross-sectional regressions because the MLE estimator takes care of the measurement error in beta. Therefore, the measurement error problem on beta is more serious than that on the market portfolio.

Keywords : Capital asset-pricing model, CAPM, Arbitrage pricing theory, Multifactor multi-indicator approach, MIMIC, Measurement error, LISREL approach, Ordinary least square, OLS, General least square, GLS, Maximum likelihood estimation, MLE

1.1.36 Chapter 37: Binomial OPM, Black-Scholes OPM and Their Relationship: Decision Tree and Microsoft Excel Approach

This chapter will first demonstrate how Microsoft Excel can be use to create the Decision Trees for the Binomial Option Pricing Model. At the same time, this chapter will discuss the Binomial Option Pricing Model in a less mathematical fashion. All the mathematical calculations will be taken care by the Microsoft Excel program that is presented in this chapter. Finally, this chapter also uses the Decision Tree approach to demonstrate the relationship between the Binomial Option Pricing Model and the Black-Scholes Option Pricing Model.

Keywords : Binomial option pricing model, Decision trees, Black-Sholes option pricing model, Call option, Put option, Microsoft Excel, Visual Basic for applications, VBA, Put-call parity, Sigma, Volatility, Recursive programming

1.1.37 Chapter 38: Dividend Payments and Share Repurchases of U.S. Firms: An Econometric Approach

This chapter uses the econometric methodology to deal with the dynamic inter-relationships between dividend payments and share repurchases and investigate endogeneity of certain explanatory variables. Identification of the model parameters is achieved in such models by exploiting the cross-equations restrictions on the coefficients in different time periods. Moreover, the estimation entails using nonlinear optimization methods to compute the maximum likelihood estimates of the dynamic random-effects models and for testing statistical hypotheses using likelihood ratio tests. This study also highlights the importance of developing comprehensive econometric models for these interrelationships. It is common in finance research to spell out “specific hypotheses” and conduct empirical research to investigate validity of the hypotheses.

Keywords : Compustat database, Corporate policies, Dividends, Dynamic random-effects models, Econometric methodology, Endogeneity, Maximum likelihood, Intangible assets, Model formulation, Nonlinear optimization, Panel data, Share repurchases

1.1.38 Chapter 39: Term Structure Modeling and Forecasting Using the Nelson-Siegel Model

In this chapter, we illustrate some recent developments in the yield curve modeling by introducing a latent factor model called the dynamic Nelson-Siegel model. This model not only provides good in-sample fit, but also produces superior out-of-sample performance. Beyond Treasury yield curve, the model can also be useful for other assets such as corporate bond and volatility. Moreover, the model also suggests generalized duration components corresponding to the level, slope, and curvature risk factors. The dynamic Nelson-Siegel model can be estimated via a one-step procedure, like the Kalman filter, which can also easily accommodate other variables of interests. Alternatively, we could estimate the model through a two-step process by fixing one parameter and estimating with ordinary least squares. The model is flexible and capable of replicating a variety of yield curve shapes: upward sloping, downward sloping, humped, and inverted humped. Forecasting the yield curve is achieved through forecasting the factors and we can impose either a univariate autoregressive structure or a vector autoregressive structure on the factors.

Keywords : Term structure, Yield curve, Factor model, Nelson-Siegel curve, State-space model

1.1.39 Chapter 40: The Intertemporal Relation Between Expected Return and Risk On Currency

The literature has so far focused on the risk-return trade-off in equity markets and ignored alternative risky assets. This chapter examines the presence and significance of an intertemporal relation between expected return and risk in the foreign exchange market. This chapter tests the existence and significance of a daily risk-return trade-off in the FX market based on the GARCH, realized, and range volatility estimators. Our empirical analysis relies on the maximum likelihood estimation of the GARCH-in-mean models, as described in Appendix A. We also use the seemingly unrelated (SUR) regressions and panel data estimation to investigate the significance of a time-series relation between expected return and risk on currency.

Keywords : GARCH, GARCH-in-mean, Seemingly unrelated regressions (SUR), Panel data estimation, Foreign exchange market, ICAPM, High-frequency data, Time-varying risk aversion, High-frequency data, Daily realized volatility

1.1.40 Chapter 41: Quantile Regression and Value-at-Risk

This chapter studies quantile regression (QR) estimation of Value-at-Risk (VaR). VaRs estimated by the QR method display some nice properties. In this chapter, different QR models in estimating VaRs are introduced. In particular, VaR estimation based on quantile regression of the QAR models, Copula models, ARCH models, GARCH models, and the CaViaR models is systematically introduced. Comparing the proposed QR method with traditional methods based on distributional assumptions, the QR method has the important property that it is robust to non-Gaussian distributions. Quantile estimation is only influenced by the local behavior of the conditional distribution of the response near the specified quantile. As a result, the estimates are not sensitive to outlier observations. Such a property is especially attractive in financial applications since many financial data like, say, portfolio returns (or log returns), are usually not normally distributed. To highlight the importance of the QR method in estimating VaR, we apply the QR techniques to estimate VaRs in International Equity Markets. Numerical evidence indicates that QR is a robust estimation method for VaR.

Keywords : ARCH, Copula, GARCH, Non-normality, QAR, Quantile regression, Risk management, Robust estimation, Time series, Value-at-risk

1.1.41 Chapter 42: Earnings Quality and Board Structure: Evidence from South East Asia

Using a sample of listed firms in Southeast Asia countries, this chapter examines the association among board structure and corporate ownership structure in affecting earnings quality. The econometric method employed is regressions of panel data. In a panel data setting, I address both cross-sectional and time-series dependence. Following Gow et al. (2010), I employ the two-way clustering method where the standard errors are clustered by both firm and year in my regressions of panel data.

Keywords : Earnings quality, Board structure, Corporate ownership structure, Panel data regressions, Cross-sectional and time-series dependence, Two-way clustering method of standard errors

1.1.42 Chapter 43: Rationality and Heterogeneity of Survey Forecasts of the Yen-Dollar Exchange Rate: A Reexamination

This chapter examines the rationality and diversity of industry-level forecasts of the yen-dollar exchange rate collected by the Japan Center for International Finance. We compare three specifications for testing rationality: the “conventional” bivariate regression, the univariate regression of a forecast error on a constant and other information set variables, and an error correction model (ECM). We extend the analysis of industry-level forecasts to a SUR-type structure using an innovative GMM technique (Bonham and Cohen 2001) that allows for forecaster cross-correlation due to the existence of common shocks and/or herd effects. Our GMM tests of micro-homogeneity uniformly reject the hypothesis that forecasters exhibit similar rationality characteristics.

Keywords : Rational expectations, Unbiasedness, Weak efficiency, Micro-homogeneity, Heterogeneity, Exchange rate, Survey forecasts, Aggregation bias, GMM, SUR

1.1.43 Chapter 44: Stochastic Volatility Structures and Intra-day Asset Price Dynamics

This chapter uses conditional volatility estimators as special cases of a general stochastic volatility structure. The theoretical asymptotic distribution of the measurement error process for these estimators is considered for particular features observed in intraday financial asset price processes. Specifically, I consider the effects of (i) induced serial correlation in returns processes, (ii) excess kurtosis in the underlying unconditional distribution of returns, (iii) market anomalies such as market opening and closing effects, and (iv) failure to account for intraday trading patterns. These issues are considered with applications in option pricing/trading strategies and the constant/dynamic hedging frameworks in mind. The methodologies used in this chapter are ARCH, maximum likelihood method, and unweighted GARCH.

Keywords : ARCH, Asymptotic distribution, Autoregressive parameters, Conditional variance estimates, Constant/dynamic hedging, Excess kurtosis, Index futures, Intraday returns, Market anomalies, Maximum likelihood estimates, Misspecification, Mis-specified returns, Persistence, Serial correlation, Stochastic volatility, Stock/futures, Unweighted GARCH, Volatility co-persistence

1.1.44 Chapter 45: Optimal Asset Allocation Under VaR Criterion: Taiwan Stock Market

This chapter examines the riskiness of the Taiwan stock market by determining the VaR from the expected return distribution generated by historical simulation. Value-at-risk (VaR) measures the worst expected loss over a given time horizon under normal market conditions at a specific level of confidence. VaR is determined by the left tail of the cumulative probability distribution of expected returns. Our result indicates the cumulative probability distribution has a fatter left tail, compared with the left tail of a normal distribution. This implies a riskier market. We also examined a two-sector asset allocation model subject to a target VaR constraint. The VaR-efficient frontier of the TAIEX traded stocks recommended, mostly, a corner portfolio.

Keywords : Value-at-risk, Asset allocation, Cumulative probability distribution, Normal distribution, VaR-efficient frontier, Historical simulation, Expected return distribution, Two-sector asset allocation model, Delta, gamma, Corner portfolio, TAIEX

1.1.45 Chapter 46: Alternative Methods for Estimating Firm’s Growth Rate

The most common valuation model is the dividend growth model. The growth rate is found by taking the product of the retention rate and the return on equity. What is less well understood are the basic assumptions of this model. In this paper, we demonstrate that the model makes strong assumptions regarding the financing mix of the firm. In addition, we discuss several methods suggested in the literature on estimating growth rates and analyze whether these approaches are consistent with the use of using a constant discount rate to evaluate the firm’s assets and equity. The literature has also suggested estimating growth rate by using the average percentage change method, compound-sum method, and/or regression methods. We demonstrate that the average percentage change is very sensitive to extreme observations. Moreover, on average, the regression method yields similar but somewhat smaller estimates of the growth rate compared to the compound-sum method. We also discussed the inferred method suggested by Gordon and Gordon ( 1997 ) to estimate the growth rate. Advantages, disadvantages, and the interrelationship among these estimation methods are also discussed in detail.

Keywords : Compound sum method, Discount cash flow model, Growth rate, Internal growth rate, Sustainable growth rate

1.1.46 Chapter 47: Econometric Measures of Liquidity

A security is liquid to the extent that an investor can trade significant quantities of the security quickly, at or near the current market price, and bearing low transaction costs. As such, liquidity is a multidimensional concept. In this chapter, I review several widely used econometrics or statistics-based measures that researchers have developed to capture one or more dimensions of a security’s liquidity (i.e., limited dependent variable model (Lesmond et al. 1999) and autocovariance of price changes (Roll 1984)). These alternative proxies have been designed to be estimated using either low-frequency or high-frequency data, so I discuss four liquidity proxies that are estimated using low-frequency data and two proxies that require high-frequency data. Low-frequency measures permit the study of liquidity over relatively long time horizons; however, they do not reflect actual trading processes. To overcome this limitation, high-frequency liquidity proxies are often used as benchmarks to determine the best low-frequency proxy. In this chapter, I find that estimates from the effective tick measure perform best among the four low-frequency measures tested.

Keywords : Liquidity, Transaction costs, Bid-ask spread, Price impact, Percent effective spread, Market model, Limited dependent variable model, Tobin’s model, Log-likelihood function, Autocovariance, Correlation analysis

1.1.47 Chapter 48: A Quasi-Maximum Likelihood Estimation Strategy for Value-at-Risk Forecasting: Application to Equity Index Futures Markets

The chapter uses GARCH model and quasi-maximum likelihood estimation strategy to investigate equity index futures markets. We present the first empirical evidence for the validity of the ARMA-GARCH model with tempered stable innovations to estimate 1-day-ahead value-at-risk in futures markets for the S&P 500, DAX, and Nikkei. We also provide empirical support that GARCH models based on the normal innovations appear not to be as well suited as infinitely divisible models for predicting financial crashes. In our empirical analysis, we forecast 1 % value-at-risk in both spot and futures markets using normal and tempered stable GARCH models following a quasi-maximum likelihood estimation strategy. In order to determine the accuracy of forecasting for each specific model, backtesting using Kupiec’s proportion of failures test is applied.

Keywords : Infinitely divisible models, Tempered stable distribution, GARCH models, Value-at-risk, Kupiec’s proportion of failures test, Quasi-maximum likelihood estimation strategy

1.1.48 Chapter 49: Computer Technology for Financial Service

This chapter examines the core computing competence for financial services. Securities trading is one of the few business activities where a few seconds of processing delay can cost a company big fortune. Grid and Cloud computing will be briefly described. How the underlying algorithm for financial analysis can take advantage of Grid environment is chosen and presented. One of the most popular practiced algorithms Monte Carlo Simulation is used in our cases study for option pricing and risk management. The various distributed computational platforms are carefully chosen to demonstrate the performance issue for financial services.

Keywords : Financial service, Grid and cloud computing, Monte Carlo simulation, Option pricing, Risk management, Cyberinfrastructure, Random number generation, High end computing, Financial simulation, Information technology

1.1.49 Chapter 50: Long-Run Stock Return and the Statistical Inference

This chapter introduces the long-run stock return methodologies and their statistical inference. The long-run stock return is usually computed by using a holding strategy more than 1 year but up to 5 years. Two categories of long-run return methods are illustrated in this chapter: the event-time approach and calendar-time approach. The event-time approach includes cumulative abnormal return, buy-and-hold abnormal return, and abnormal returns around earnings announcements. In former two methods, it is recommended to apply the empirical distribution (from the bootstrapping method) to examine the statistical inference, whereas the last one uses classical t -test. In addition, the benchmark selections in the long-run return literature are introduced. Moreover, the calendar-time approach contains mean monthly abnormal return, factor models, and Ibbotson’s RATS, which could be tested by time-series volatility.

Keywords : Long-run stock return, Buy-and-hold return, Factor model, Event-time, Calendar-time, Cumulative abnormal return, Ibottson’s RATS, Conditional market model, Bootstrap, Zero-investment portfolio

1.1.50 Chapter 51: Value-at-Risk Estimation via a Semi-Parametric Approach: Evidence from the Stock Markets

This study utilizes the parametric approach (GARCH-based models) and the semi-parametric approach of Hull and White (1998) (HW-based models) to estimate the Value-at-Risk (VaR) through the accuracy evaluation of accuracy for the eight stock indices in Europe and Asia stock markets. The measure of accuracy includes the unconditional coverage test by Kupiec (1995) as well as two loss functions, quadratic loss function and unexpected loss. As to the parametric approach, the parameters of generalized autoregressive conditional heteroskedasticity (GARCH) model are estimated by the method of maximum likelihood and the quantiles of asymmetric distribution like skewed generalized student’s t (SGT) can be solved by composite trapezoid rule. Sequentially, the VaR is evaluated by the framework proposed by Jorion (2000). Turning to the semi-parametric approach of Hull and White (1998), before performing the traditional historical simulation, the raw return series is scaled by a volatility ratio where the volatility is estimated by the same procedure of parametric approach.

Keywords : Value-at-risk, Semi-parametric approach, Parametric approach, Generalized autoregressive conditional heteroskedasticity, Skewed generalized student’s t , Composite trapezoid rule, Method of maximum likelihood, Unconditional coverage test, Loss function

1.1.51 Chapter 52: Modeling Multiple Asset Returns by a Time-Varying t Copula Model

This chapter illustrates a framework to model joint distributions of multiple asset returns using a time-varying Student’s t copula model. We model marginal distributions of individual asset returns by a variant of GARCH models and then use a Student’s t copula to connect all the margins. To build a time-varying structure for the correlation matrix of t copula, we employ a dynamic conditional correlation (DCC) specification. We illustrate the two-stage estimation procedures for the model and apply the model to 45 major US stocks returns selected from nine sectors. As it is quite challenging to find a copula function with very flexible parameter structure to account for difference dependence features among all pairs of random variables, our time-varying t copula model tends to be a good working tool to model multiple asset returns for risk management and asset allocation purposes. Our model can capture time-varying conditional correlation and some degree of tail dependence, while it also has limitations of featuring symmetric dependence and inability of generating high tail dependence when being used to model a large number of asset returns.

Keywords : Student’s t copula, GARCH models, Asset returns, U.S. stocks, Maximum likelihood, Two-stage estimation, Tail dependence, Exceedance correlation, Dynamic conditional correlation, Asymmetric dependence

1.1.52 Chapter 53: Internet Bubble Examination with Mean-Variance Ratio

This chapter illustrates the superiority of the mean-variance ratio (MVR) test over the traditional SR test by applying both tests to analyze the performance of the S&P 500 index and the NASDAQ 100 index after the bursting of the Internet bubble in 2000s. This shows the superiority of the MVR test statistic in revealing short-term performance and, in turn, enables investors to make better decisions in their investments. The methodologies used in this chapter are mean-variance ratio, Sharpe ratio, hypothesis testing, and uniformly most powerful unbiased test.

Keywords : Mean-variance ratio, Sharpe ratio, Hypothesis testing, Uniformly most powerful unbiased test, Internet bubble, Fund management

1.1.53 Chapter 54: Quantile Regression in Risk Calibration

This chapter uses the CoVaR (Conditional VaR) framework to obtain accurate information on the interdependency of risk factors. The basic technical elements of CoVaR estimation are two levels of quantile regression: one on market risk factors; another on individual risk factor. Tests on the functional form of the two-level quantile regression reject the linearity. A flexible semi-parametric modeling framework for CoVaR is proposed. A partial linear model (PLM) is analyzed. In applying the technology to stock data covering the crisis period, the PLM outperforms in the crisis time, with the justification of the backtesting procedures. Moreover, using the data on global stock markets indices, the analysis on marginal contribution of risk (MCR) defined as the local first order derivative of the quantile curve sheds some light on the source of the global market risk.

Keywords : CoVAR, Value-at-risk, Quantile regression, Locally linear quantile regression, Partial linear model, Semi-parametric model

1.1.54 Chapter 55: Strike Prices of Options for Overconfident Executives

This chapter uses Monte Carlo simulation to investigate the impacts of managerial overconfidence on the optimal strike prices of executive incentive options. Although it has been shown that optimally managerial incentive options should be awarded in-the-money, in practice, most firms award them at-the-money. We show that the optimal strike prices of options granted to overconfident executive are directly related to their overconfidence level, and that this bias brings the optimal strike prices closer to the institutionally prevalent at-the-money prices. The Monte Carlo simulation procedure uses a Mathematica program to find the optimal effort by managers and the optimal (for stockholders) contract parameters. An expanded discussion of the simulations, including the choice of the functional forms and the calibration of the parameters, is provided.

Keywords : Overconfidence, Managerial effort, Incentive options, Strike price, Simulations, Behavioral finance, Executive compensation schemes, Mathematica optimization, Risk aversion, Effort aversion

1.1.55 Chapter 56: Density and Conditional Distribution Based Specification Analysis

This chapter uses densities and conditional distributions analysis to carry out consistent specification testing and model selection among multiple diffusion processes. In this chapter, we discuss advances to this literature introduced by Corradi and Swanson (2005), who compare the cumulative distribution (marginal or joint) implied by a hypothesized null model with corresponding empirical distributions of observed data. In particular, parametric specification tests in the spirit of the conditional Kolmogorov test of Andrews (1997) that rely on block bootstrap resampling methods in order to construct test critical values are discussed. The methodologies used in this chapter are continuous time simulation methods, single process specification testing, multiple process model selection, and multifactor diffusion process, block bootstrap, and jump process.

Keywords : Multifactor diffusion process, Specification test, Out-of-sample forecasts, Conditional distribution, Model selection, Block bootstrap, Jump process

1.1.56 Chapter 57: Assessing the Performance of Estimators Dealing with Measurement Errors

This chapter describes different procedures to deal with measurement error in linear models, and assess their performance in finite samples using Monte Carlo simulations, and data on corporate investment. We consider the standard instrumental variables approach proposed by Griliches and Hausman (1986) as extended by Biorn (2000) [OLS-IV], the Arellano and Bond (1991) instrumental variable estimator, and the higher-order moment estimator proposed by Erickson and Whited (2000, 2002). Our analysis focuses on characterizing the conditions under which each of these estimators produces unbiased and efficient estimates in a standard “errors in variables” setting. In the presence of fixed effects, under heteroscedasticity, or in the absence of a very high degree of skewness in the data, the EW estimator is inefficient and returns biased estimates for mismeasured and perfectly measured regressors. In contrast to the EW estimator, IV-type estimators (OLS-IV and AB-GMM) easily handle individual effects, heteroskedastic errors, and different degrees of data skewness. The IV approach, however, requires assumptions about the autocorrelation structure of the mismeasured regressor and the measurement error. We illustrate the application of the different estimators using empirical investment models.

Keywords : Investment equations, Measurement error, Monte Carlo simulations, Instrumental variables, GMM, Bias, Fixed effects, Heteroscedasticity, Skewness, High-order moments

1.1.57 Chapter 58: Realized Distributions of Dynamic Conditional Correlation and Volatility Thresholds in the Crude Oil, Gold, and Dollar/Pound Currency Markets

This chapter proposes a modeling framework for the study of co-movements in price changes among crude oil, gold, and dollar/pound currencies that are conditional on volatility regimes. Methodologically, we extend the Dynamic Conditional Correlation (DCC) multivariate GARCH model to examine the volatility and correlation dynamics depending on the variances of price returns involving a threshold structure. The results indicate that the periods of market turbulence are associated with an increase in co-movements in commodity (gold and oil) prices. The results imply that gold may act as a safe haven against major currencies when investors face market turmoil.

Keywords : Dynamic conditional correlation, Volatility threshold, Realized distribution, Currency market, Gold, Oil

1.1.58 Chapter 59: Pre-IT Policy, Post-IT Policy, and the Real Sphere in Turkey

We estimate Two SVECM (Structural Vector Error Correction) Models for the Turkish economy based on imposing short run and Long-run restrictions that accounts for examining the behavior of the real sphere in the Pre-IT policy (before Inflation-Targeting adoption) and Post-IT policy (after Inflation-Targeting Adoption). Responses reveals that an expansionary interest policy shock leads to a decrease in price level, a fall in output, an appreciation in the exchange rate, an improvement in the share prices in the very short run for the most of Pre-IT period.

Keywords : SVECM models, Turkish economy, Short run, Long run, Restrictions, Inflation targeting, Pre-IT policy, Post-IT policy, Share prices, Exchange rate, Monetary policy shock, Output, Price level, Real sphere

1.1.59 Chapter 60: Determination of Capital Structure: A LISREL Model Approach

In this chapter, we employ structural equation modeling (SEM) in LISREL system to solve the measurement errors problems in the analysis of the determinants of capital structure and find the important factors consistent with capital structure theory by using date from 2002 to 2010. The purpose of this chapter is to investigate whether the influences of accounting factors on capital structure change and whether the important factors are consistent with the previous literature. The methodologies discussed in this chapter are structural equation modeling (SEM), multiple indicators and multiple causes (MIMIC) model, LISREL system, simultaneous equations, and SEM with confirmatory factor analysis (CFA) approach.

Keywords : Capital structure, Structural equation modeling (SEM), Multiple indicators and multiple causes (MIMIC) model, LISREL system, Simultaneous equations, Latent variable, Determinants of capital structure, Error in variable problem

1.1.60 Chapter 61: Evaluating the Effectiveness of Futures Hedging

This chapter examines the Ederington hedging effectiveness (EHE) comparisons between unconditional OLS hedge strategy and other conditional hedge strategies. It is shown that OLS hedge strategy outperforms most of the optimal conditional hedge strategies when EHE is used as the hedging effectiveness criteria. Before concluding that OLS hedge is better than the others; however, we need to understand under what circumstances the result is derived. We explain why OLS is the best hedge strategy under EHE criteria in most cases, and how most conditional hedge strategies are judged as inferior to OLS hedge strategy by an EHE comparison.

Keywords : Futures hedging, Portfolio management, Ederington hedging effectiveness, Variance estimation, Unconditional variance, Conditional variance, OLS hedging strategy, GARCH hedging strategy, Regime-switching hedging strategy, Utility-based hedging strategy

1.1.61 Chapter 62: Evidence on Earning Management by Integrated Oil and Gas Companies

This chapter uses Jones Model ( 1991 ) which projects the expected level of discretionary accruals and demonstrates specific test methodology for detection of earnings management in the oil and gas industry. This study utilized several parametric and nonparametric statistical methods to test for such earnings management. By comparing actuals versus projected accruals, we are able to compute the total unexpected accruals. We also correlate unexpected total accruals with several difficult to manipulate indicators that reflect company’s level of activities.

Keywords : Earning management, Jones ( 1991 ) model, Discretionary accruals, Income from operations, Nonrecurring items, Special items, Research and development expense, Write-downs, Political cost, Impression management, Oil and gas industry

1.1.62 Chapter 63: A Comparative Study of Two Models SV with MCMC Algorithm

This chapter examines two asymmetric stochastic volatility models used to describe the volatility dependencies found in most financial returns. The first is the autoregressive stochastic volatility model with Student’s t-distribution (ARSV-t), and the second is the basic Svol of JPR (1994). In order to estimate these models, our analysis is based on the Markov Chain Monte Carlo (MCMC) method. Therefore, the technique used is a Metropolishastings (Hastings 1970), and the Gibbs sampler (Casella and George 1992; Gelfand and Smith 1990; Gilks et al. 1993). The empirical results concerned on the Standard and Poor’s 500 composite Index (S&P), CAC40, Nasdaq, Nikkei, and Dow-Jones stock price indexes reveal that the ARSV-t model provides a better performance than the Svol model on the Mean Squared Error (MSE) and the Maximum Likelihood function.

Keywords : Autoregression, Asymmetric stochastic volatility, MCMC, Metropolishastings, Gibbs sampler, Volatility dependencies, Student’s t-distribution, SVOL, MSE, Financial returns, Stock price indexes

1.1.63 Chapter 64: Internal Control Material Weakness, Analysts’ Accuracy and Bias, and Brokerage Reputation

This chapter uses the Ordinary Least-Squares (OLS) methodology in the main tests to examine the impact of internal control material weaknesses (ICMW hereafter) on sell side analysts. We match our ICMW firms with non-ICMWs based on industry, sales, and assets. We re-estimate the models using rank regression technique to assess the sensitivity of the results to the underlying functional form assumption made by OLS. We use Cook’s distance to test the outliers.

Keywords : Internal control material weakness, Analyst forecast accuracy, Analyst forecast bias, Brokerage reputation, Sarbanes-Oxley act, Ordinary least squares regressions, Rank regressions, Fixed effects, Matching procedure, Cook’s distance

1.1.64 Chapter 65: What Increases Banks’ Vulnerability to Financial Crisis: Short-Term Financing or Illiquid Assets?

This chapter applies Logit and OLS econometric techniques to analyze the Federal Reserve Y-9C report data. We show that short-term financing is a response to the adverse economic shocks rather than a cause of the recent crisis. The likelihood of financial crisis actually stems from the illiquidity and low creditworthiness of the investment. Our results are robust to endogeneity concerns when we use a difference-in-differences (DiD) approach with the Lehman bankruptcy in 2008 proxying for an exogenous shock.

Keywords : Financial crisis, Short-term financing, Debt maturity, Liquidity risk, Deterioration of bank asset quality

1.1.65 Chapter 66: Accurate Formulae for Evaluating Barrier Options with Dividends Payout and the Application in Credit Risk Valuation

This chapter approximates the discrete dividend payout by a stochastic continuous dividend yield, so the post dividend stock price process can be approximated by another log-normally diffusive stock process with a stochastic continuous payout ratio up to the ex-dividend date. Accurate approximation analytical pricing formulae for barrier options are derived by repeatedly applying the reflection principle. Besides, our formulae can be applied to extend the applicability of the first passage model – a branch of structural credit risk model. The stock price falls due to the dividend payout in the option pricing problem is analog to selling the firm’s asset to finance the loan repayment or dividend payout in the first passage model. Thus, our formulae can evaluate vulnerable bonds or the equity values given that the firm’s future loan/dividend payments are known.

Keywords : Barrier option, Option pricing, Stock option, Dividend, Reflection principle, Lognormal, Credit risk

1.1.66 Chapter 67: Pension Funds: Financial Econometrics on the Herding Phenomenon in Spain and the United Kingdom

This chapter uses the estimated cross-sectional standard deviations of betas to analyze if manager’s behavior enhances the existence of herding phenomena and the impact of the Spanish and UK pension funds investment on the market efficiency. We also estimate the betas with an econometric technique less applied in the financial literature: state-space models and the Kalman filter. Additionally, in order to obtain a robust estimation, we apply the Huber estimator. Finally, we apply several models and study the existence of herding toward the market, size, book-to-market, and momentum factors.

Keywords : Herding, Pension funds, State-space models, Kalman filter, Huber estimation, Imitation, Behavioral finance, Estimated cross-sectional standard deviations of betas, Herding toward the market, Herding toward size factor, Herding toward book-to-market factor, and Herding toward momentum factor

1.1.67 Chapter 68: Estimating the Correlation of Asset Returns: A Quantile Dependence Perspective

This chapter uses the Copula Quantile - on - Quantile Regression (C-QQR) approach to construct the correlation between the conditional quantiles of stock returns. This new approach of estimating correlation utilizes the idea that the condition of a stock market is related to its return performance, particularly to the conditional quantile of its return, as the lower return quantiles reflect a weak market while the upper quantiles reflect a bullish one. The C-QQR approach uses the copula to generate a regression function for modeling the dependence between the conditional quantiles of the stock returns under consideration. It is estimated using a two-step quantile regression procedure, where in principle, the first step is implemented to model the conditional quantile of one stock return, which is then related in the second step to the conditional quantile of another return.

Keywords : Stock markets, Copula, Correlation, Quantile regression, Quantile dependence, Business cycle, Dynamics, Risk management, Investment, Tail risk, Extreme events, Market uncertainties

1.1.68 Chapter 69: Multi-criteria Decision Making for Evaluating Mutual Funds Investment Strategies

This chapter uses the criteria measurements to evaluate investment style and investigate multiple criteria decision-making (MCDM) problem. To achieve this objective, first, we employ factor analysis to extract independent common factors from those criteria. Second, we construct the evaluation frame using hierarchical system composed of the above common factors with evaluation criteria, and then derive the relative weights with respect to the considered criteria. Third, the synthetic utility value corresponding to each investment style is aggregated by the weights with performance values. Finally, we compare with empirical data and find that the model of MCDM predicts the rate of return.

Keywords : Investment strategies, Multiple Criteria Decision Making (MCDM), Hierarchical system, Investment style, Factor analysis, Synthetic utility value, Performance values

1.1.69 Chapter 70: Econometric Analysis of Currency Carry Trade

This chapter investigates carry trade strategy in the currency markets whereby investors fund positions in high interest rate currencies by selling low interest rate currencies to earn the interest rate differential. In this chapter, we first provide an overview of the risk and return profile of currency carry trade; second, we introduce two popular models, the regime-switch model and the logistic smooth transition regression model, to analyze carry trade returns because the carry trade returns are highly regime dependent. Finally, an empirical example is illustrated.

Keywords : Carry trade, Uncovered interest parity, Markov chain Monte Carlo, Regime-switch model, Logistic smooth transition regression model

1.1.70 Chapter 71: Analytical Bounds for Treasury Bond Futures Prices

This study employs a maximum likelihood estimation technique presented by Chen and Scott (1993) to estimate the parameters for two-factor Cox-Ingersoll-Ross models of the term structure. Following the estimation, the factor values are solved for by matching the short rate with the cheapest-to-deliver bond price. Then, upper bounds and lower bounds for Treasury bond futures prices can be calculated. This study first shows that the popular preference-free, closed-form cost of carry model is an upper bound for the Treasury bond futures price. Then, the next step is to derive analytical lower bounds for the futures price under one- and two-factor Cox-Ingersoll-Ross models of the term structure.

Keywords : Treasury bond futures, Delivery options, Cox-Ingersoll-Ross models, Bounds, Maximum likelihood estimation, Term structure, Cheapest-to-deliver bond, Timing options, Quality options, Chicago board of trade

1.1.71 Chapter 72: Rating Dynamics of Fallen Angels and Their Speculative Grade-Rated Peers: Static Versus Dynamic Approach

This study adopts the survival analysis framework (Allison 1984) to examine issuer-heterogeneity and time-heterogeneity in the rating migrations of fallen angels (FAs) and their speculative grade-rated peers (FA peers). Cox’s hazard model is considered the pre-eminent method to estimate the probability that an issuer survives in its current rating grade at any point in time t over the time horizon T. In this study, estimation is based on two Cox’s hazard models, including a proportional hazard model (Cox 1972) and a dynamic hazard model. The first model employs a static estimation approach and time-independent covariates, whereas the second uses a dynamic estimation approach and time-dependent covariates. To allow for any dependence among rating states of the same issuer, the marginal event-specific method (Wei et al. 1989) was used to obtain robust variance estimates. For validation purpose, the Brier score (Brier 1950) and its covariance decomposition (Yates 1982) were applied to assess the forecast performance of estimated models in forming time-varying survival probability estimates for issuers out-of-sample.

Keywords : Survival analysis, Hazard model, Time-varying covariate, Recurrent event, Brier score, Covariance decomposition, Rating migration, Fallen angel, Markov property, Issuer-heterogeneity, Time-heterogeneity

1.1.72 Chapter 73: Creation and Control of Bubbles: Managers Compensation Schemes, Risk Aversion, and Wealth and Short Sale Constraints

This chapter takes an alternative approach of inquiry – that of using laboratory experiments – to study the creation and control of speculative bubbles. The following three factors are chosen for analysis: the compensation scheme of portfolio managers, wealth and supply constraints, and the relative risk aversion of traders. Under a short investment horizon induced by a tournament compensation scheme, speculative bubbles are observed in markets of speculative traders and in mixed markets of conservative and speculative traders. The primary method of analysis is to use live subjects in a laboratory setting to generate original trading data, which are compared to their fundamental values. Standard statistical techniques are used to supplement analysis in explaining the divergence of asset prices from their fundamental values.

Keywords : Speculative bubbles, Laboratory experimental asset markets, Fundamental asset values, Tournament, Market efficiency, Behavioral finance, Ordinary least squares regression, Correlation

1.1.73 Chapter 74: Range Volatility: A Review of Models and Empirical Studies

In this chapter, we survey the significant development of range-based volatility models, beginning with the simple random walk model up to the conditional autoregressive range (CARR) model. For the extension to range-based multivariate volatilities, some approaches developed recently are adopted, such as the dynamic conditional correlation (DCC) model, the double smooth transition conditional correlation (DSTCC) GARCH model, and the copula method. At last, we introduce different approaches to build bias-adjusted realized range to obtain a more efficient estimator.

Keywords : Range, Volatility forecasting, Dynamic conditional correlation, Smooth transition, Copula, Realized volatility, Risk management

1.1.74 Chapter 75: Business Models: Applications to Capital Budgeting, Equity Value, and Return Attribution

This chapter describes a business model in a contingent claim modeling framework. The chapter then provides three applications of the business model. Firstly, the chapter determines the optimal capital budgeting decision in the presence of fixed operating costs, and shows how the fixed operating cost should be accounted by in an NPV calculation. Secondly, the chapter determines the values of equity value, the growth option, the retention option as the building blocks of primitive firm value. Using a sample of firms, the chapter illustrates a method in comparing the equity values of firms in the same business sector. Thirdly, the chapter relates the change in revenue to the change in equity value, showing how the combined operating leverage and financial leverage may affect the firm valuation and risks.

Keywords : Bottom-up capital budgeting, Business model, Capital budgeting, Contingent claim model, Equity value, Financial leverage, Fixed operating cost, Gross return on investment (GRI), Growth option, Market performance measure, NPV, Operating leverage, Relative value of equity, Retention option, Return attribution, Top-down capital budgeting, Wealth transfer

1.1.75 Chapter 76: VAR Models: Estimation, Inferences, and Applications

This chapter provides a brief overview of the basic Vector autoregression (VAR) approach by focusing on model estimation and statistical inferences. VAR models have been used extensive in finance and economic analysis. Applications of VAR models in some finance areas are discussed, including asset pricing, international finance, and market microstructure. It is shown that such approach provides a powerful tool to study financial market efficiency, stock return predictability, exchange rate dynamics, and information content of stock trades and market quality.

Keywords : VAR, Granger-causality test, Impulse response, Variance decomposition, Co-integration, Asset return predictability, Market quality, Information content of trades, Informational efficiency

1.1.76 Chapter 77: Model Selection for High-Dimensional Problems

This chapter introduces penalized least squares, which seek to keep important predictors in a model, while penalizing coefficients associated with irrelevant predictors. As such, under certain conditions, penalized least squares can lead to a sparse solution for linear models and achieve asymptotic consistency in separating relevant variables from irrelevant ones. We then review independence screening, a recently developed method for analyzing ultrahigh-dimensional data where the number of variables or parameters can be exponentially larger than the sample size. Independence screening selects relevant variables based on certain measures of marginal correlations between candidate variables and the response. Finally, we discuss and advocate multistage procedures that combine independence screening and variable selection and that may be especially suitable for analyzing high-frequency financial data.

Keywords : Model selection, Variable selection, Dimension reduction, Independence screening, High-dimensional data, Ultrahigh-dimensional data, Generalized correlations, Penalized least squares, Shrinkage, Statistical learning, SCAD penalty, Oracle property

1.1.77 Chapter 78: Hedonic Regression Models

The chapter examines three specific, different hedonic specifications: the linear, semi-log, and Box-Cox transformed hedonic models and applies them to real estate data. It also discusses recent innovations related to hedonic models and how these models are being used in contemporary studies. This provides a basic overview of the nature and variety of hedonic empirical pricing models that are employed in the economics literature. It explores the history of hedonic modeling and summarizes the field’s utility-theory-based, microeconomic foundations. It also provides a discussion of and potential solutions for common problems associated with hedonic modeling.

Keywords : Hedonic models, Regression, Real estate, Box-Cox, Pricing, Price indexes, Semi-log, Least squares, Housing, Property

1.1.78 Chapter 79: Optimal Payout Ratio Under Uncertainty and the Flexibility Hypothesis: Theory and Empirical Evidence

We theoretically extend the proposition of DeAngelo and DeAngelo’s (2006) optimal payout policy in terms of the flexibility dividend hypothesis. We also introduce growth rate, systematic risk, and total risk variables into the theoretical model. We use a panel data collected in the USA from 1969 to 2009 to empirically investigate the impact of growth rate, systematic risk, and total risk on the optimal payout ratio in terms of the fixed-effects model. Furthermore, we implement the moving estimates process to find the empirical breakpoint of the structural change for the relationship between the payout ratio and risks and confirm that the empirical breakpoint is not different from our theoretical breakpoint. Our theoretical model and empirical results can therefore be used to identify whether flexibility or the free cash flow hypothesis should be used to determine the dividend policy.

Keywords : Dividends, Payout policy, Optimal payout ratio, Flexibility hypothesis, Free cash flow hypothesis, Signaling hypothesis, Fixed effect, Clustering effect, Structural change model, Moving estimates processes, Systematic risk, Total risk, Market perfection

1.1.79 Chapter 80: Modeling Asset Returns with Skewness, Kurtosis, and Outliers

This chapter uses an exponential generalized beta distribution of the second kind (EGB2) to model the returns on 30 Dow-Jones industrial stocks. The model accounts for stock return characteristics, including fat tails, peakedness (leptokurtosis), skewness, clustered conditional variance, and leverage effect. The goodness-of-fit statistic provides supporting evidence in favor of EGB2 distribution in modeling stock returns. The EGB2 distribution used in this chapter is a four parameter distribution. It has a closed-form density function, and its higher-order moments are finite and explicitly expressed by its parameters. The EGB2 distribution nests many widely used distributions such as normal distribution, log-normal distribution, Weibull distribution, and standard logistic distribution.

Keywords : Expected stock return, Higher moments, EGB2 distribution, Risk management, Volatility, Conditional skewness, Risk premium

1.1.80 Chapter 81: Does Revenue Momentum Drive or Ride Earnings or Price Momentum?

This chapter performs dominance test to show that revenue surprises, earnings surprises, and prior returns, each lead to significant momentum returns that cannot be fully explained by the others, suggesting that each convey some exclusive and unpriced information content. Also, the joint implications of revenue surprises, earnings surprises, and prior returns are underestimated by investors, particularly when information variables point in the same direction. Momentum cross-contingencies are observed in that momentum profits driven by firm fundamental information positively depend on the accompanying firm market information, and vice versa. A three-way combined momentum strategy may offer monthly return as high as 1.44%.

Keywords : Earnings surprises, Momentum strategies, Post-earnings-announcement drift, Revenue surprises

1.1.81 Chapter 82: A VG-NGARCH Model for Impacts of Extreme Events on Stock Returns

This chapter compares two types of GARCH models, namely, the VG-NGARCH and the GARCH-jump model with autoregressive conditional jump intensity, i.e., the GARJI model, to make inferences on the log of stock returns when there are irregular substantial price fluctuations. The VG-NGARCH model imposes a nonlinear asymmetric structure on the conditional shape parameters in a variance-gamma process, which describes the arrival rates for news with different degrees of influence on price movements, and provides an ex ante probability for the occurrence of large price movements. On the other hand, the GARJI model, a mixed GARCH-jump model proposed by Chan and Maheu (2002), adopts two independent autoregressive processes to model the variances corresponding to moderate and large price movements, respectively.

Keywords : VG-NGARCH model, GARCH-jump model, Autoregressive conditional jump intensity, GARJI model, Substantial price fluctuations, Shape parameter, Variance-gamma process, Ex ante probability, Daily stock price, Goodness-of-fit

1.1.82 Chapter 83: Risk-Averse Portfolio Optimization via Stochastic Dominance Constraints

This chapter presents a new approach to portfolio selection based on stochastic dominance. The portfolio return rate in the new model is required to stochastically dominate a random benchmark. We formulate optimality conditions and duality relations for these models and construct equivalent optimization models with utility functions. Two different formulations of the stochastic dominance constraint, primal and inverse, lead to two dual problems which involve von Neuman–Morgenstern utility functions for the primal formulation and rank dependent (or dual) utility functions for the inverse formulation. We also discuss the relations of our approach to value-at-risk and conditional value-at-risk.

Keywords : Portfolio optimization, Stochastic dominance, Stochastic order, Risk, Expected utility, Duality, Rank dependent utility, Yaari’s dual utility, Value-at-risk, Conditional value-at-risk

1.1.83 Chapter 84: Implementation Problems and Solutions in Stochastic Volatility Models of the Heston Type

This chapter compares three major approaches to solve the numerical instability problem inherent in the fundamental solution of the Heston model. In this chapter, we used the fundamental transform method proposed by Lewis to reduce the number of variables from two to one and separate the payoff function from the calculation of the Green function for option pricing. We show that the simple adjusted-formula method is much simpler than the rotation-corrected angle method of Kahl and Jäckel and also greatly superior to the direct integration method of Shaw if taking computing time into consideration.

Keywords : Heston, Stochastic volatility, Fourier inversion, Fundamental transform, Complex logarithm, Rotation-corrected angle, Simple adjusted formula, Green function

1.1.84 Chapter 85: Stochastic Change-Point Models of Asset Returns and Their Volatilities

This chapter considers two time-scales and uses the “short” time-scale to define GARCH dynamics and the “long” time-scale to incorporate parameter jumps. This leads to a Bayesian change-point ARX-GARCH model, whose unknown parameters may undergo occasional changes at unspecified times and can be estimated by explicit recursive formulas when the hyperparameters of the Bayesian model are specified. Efficient estimators of the hyperparameters of the Bayesian model can be developed. The empirical Bayes approach can be applied to the frequentist problem of partitioning the time series into segments under sparsity assumptions on the change-points.

Keywords : ARX-GARCH, Bounded complexity, Contemporaneous jumps, Change-point models, Empirical Bayes, Frequentist segmentation, Hidden Markov models, Hyperparameter estimation, Markov chain Monte Carlo, Recursive filters, Regression models, Stochastic volatility

1.1.85 Chapter 86: Unspanned Stochastic Volatilities and Interest Rate Derivatives Pricing

This chapter first reviews the recent literature on the Unspanned Stochastic Volatilities (USV) documented in the interest rate derivatives markets. The USV refers to the volatilities factors implied in the interest rate derivatives prices that have little correlation with the yield curve factors. We then present the result in Li and Zhao (2006) that a sophisticated DTSM without USV feature can have serious difficulties in hedging caps and cap straddles, even though they capture bond yields well. Furthermore, at-the-money straddle hedging errors are highly correlated with cap-implied volatilities and can explain a large fraction of hedging errors of all caps and straddles across moneyness and maturities. We also present a multifactor term structure model with stochastic volatility and jumps that yields a closed-form formula for cap prices from Jarrow et al. (2007). The three-factor stochastic volatility model with Poisson jumps can price interest rate caps well across moneyness and maturity. The econometric methods in this chapter include extended Kalman filtering, maximum likelihood estimation with latent variables, local polynomial method, and nonparametric density estimation.

Keywords : Term structure modeling, Interest rate volatility, Heath-Jarrow-Morton model, Nonparametric density estimation, Extended Kalman filtering

1.1.86 Chapter 87: Alternative Equity Valuation Models

This chapter examines alternative equity valuation models and their ability to forecast future stock prices. We use simultaneous equations estimation technique to investigate the stock price forecast ability of Ohlson’s model, Feltham and Ohlson’s Model, and Warren and Shelton’s (1971) model. Moreover, we use the combined forecasting methods proposed by Granger and Newbold ( 1973 ) and Granger and Ramanathan ( 1984 ) to form combined stock price forecasts from individual models. Finally, we examine whether comprehensive earnings can provide incremental price-relevant information beyond net income.

Keywords : Ohlson model, Feltham and Ohlson model, Warren and Shelton model, Equity valuation models, Simultaneous equations estimation, Fundamental analysis, Financial statement analysis, Financial planning and forecasting, Combined forecasting, Comprehensive earnings, Abnormal earnings, Operating earnings, Accounting earnings

1.1.87 Chapter 88: Time Series Models to Predict the Net Asset Value (NAV) of an Asset Allocation Mutual Fund VWELX

This research examines the use of various forms of time-series models to predict the total net asset value (NAV) of an asset allocation mutual fund. The first set of model structures included simple exponential smoothing, double exponential smoothing, and the Winter’s method of smoothing. The second set of predictive models used represented trend models. They were developed using regression estimation. They included linear trend model, quadratic trend model, and an exponential model. The third type of method used was a moving average method. The fourth set of models incorporated the Box-Jenkins method, including an autoregressive model, a moving average model, and an unbounded autoregressive and moving average method.

Keywords : NAV of a mutual fund, Asset allocation fund, Combination of forecasts, Single exponential smoothing, Double exponential smoothing, Winter’s method, Linear trend model, Quadratic trend model, Exponential trend model, Moving average method, Autoregressive model, Moving average model, Unbounded autoregressive moving average model

1.1.88 Chapter 89: Discriminant Analysis and Factor Analysis: Theory and Method

This chapter discusses three multivariate techniques in detail: discriminant analysis, factor analysis, and principal component analysis. In addition, the stepwise discriminant analysis by Pinches and Mingo ( 1973 ) is improved using a goal programming technique. These methodologies are applied to determine useful financial ratios and the subsequent bond ratings. The analysis shows that the stepwise discriminant analysis fails to be an efficient solution as the hybrid approach using the goal programming technique outperforms it, which is a compromised solution for the maximization of the two objectives, namely, the maximization of the explanatory power and the maximization of discriminant power.

Keywords : Multivariate technique, Discriminant analysis, Factor analysis, Principle component analysis, Stepwise discriminant analysis, Goal programming, Bond ratings, Compromised solution, Explanatory power, Discriminant power

1.1.89 Chapter 90: Implied Volatility: Theory and Empirical Method

This chapter reviews the different theoretical methods used to estimate implied standard deviation and to show how the implied volatility can be estimated in empirical work. The OLS method for estimating implied standard deviation is first introduced and the formulas derived by applying a Taylor series expansion method to Black-Scholes option pricing model are also described. Three approaches of estimating implied volatility are derived from one, two, and three options, respectively. Because of these formulas with the remainder terms, the accuracy of these formulas depends on how an underlying asset is close to the present value of exercise price in an option. The formula utilizing three options for estimating implied volatility is more accurate rather than other two approaches. In this chapter, we use call options on S&P 500 index futures in 2010 and 2011 to illustrate how MATLAB can be used to deal with the issue of convergence in estimating implied volatility of future options.

Keywords : Implied volatility, Implied standard deviation (ISD), Option pricing model, MATLAB, Taylor series expansion, Ordinary least-squares (OLS), Black-Scholes Model, Options on S&P 500 index futures

1.1.90 Chapter 91: Measuring Credit Risk in a Factor Copula Model

This chapter uses a new approach to estimate future credit risk on target portfolio based on the framework of CreditMetricsTM by J.P. Morgan. However, we adopt the perspective of factor copula and then bring the principal component analysis concept into factor structure to construct a more appropriate dependence structure among credits. In order to examine the proposed method, we use real market data instead of a virtual one. We also develop a tool for risk analysis which is convenient to use, especially for banking loan businesses. The results show the fact that people assume dependence structures are normally distributed will indeed lead to risks underestimate. On the other hand, our proposed method captures better features of risks and shows the fat-tail effects conspicuously even though assuming the factors are normally distributed.

Keywords : Credit risk, Credit VaR, Default correlation, Copula, Factor copula, Principal component analysis

1.1.91 Chapter 92: Instantaneous Volatility Estimation by Nonparametric Fourier Transform Methods

This chapter conducts some simulation tests to justify the effectiveness of the Fourier transform method. Malliavin and Mancino (2009) proposed a nonparametric Fourier transform method to estimate the instantaneous volatility under the assumption that the underlying asset price process is a semi-martingale. Two correction schemes are proposed to improve the accuracy of volatility estimation. By means of these Fourier transform methods, some documented phenomena such as volatility daily effect and multiple risk factors of volatility can be observed. Then, a linear hypothesis between the instantaneous volatility and VIX derived from Zhang and Zhu (2006) is investigated.

Keywords : Information content, Instantaneous volatility, Fourier transform method, Bias reduction, Correction method, Local volatility, Stochastic volatility, VIX, Volatility daily effect, Online estimation

1.1.92 Chapter 93: A Dynamic CAPM with Supply Effect: Theory and Empirical Results

This chapter first theoretically extends Black’s CAPM, and then uses price, dividend per share, and earnings per share to test the existence of supply effect with US equity data. A simultaneous equation system is constructed through a standard structural form of a multi-period equation to represent the dynamic relationship between supply and demand for capital assets. The equation system is exactly identified under our specification. Then, two hypotheses related to supply effect are tested regarding the parameters in the reduced-form system. The equation system is estimated by the Seemingly Unrelated Regression (SUR) method, since SUR allows one to estimate the presented system simultaneously while accounting for the correlated errors.

Keywords : CAPM, Asset, Endogenous supply, Simultaneous equations

1.1.93 Chapter 94: A Generalized Model for Optimum Futures Hedge Ratio

This chapter proposes the generalized hyperbolic distribution as the joint log-return distribution of the spot and futures. Using the parameters in this distribution, we derive several most widely used optimal hedge ratios: minimum variance, maximum Sharpe measure, and minimum generalized semivariance. To estimate these optimal hedge ratios, we first write down the log-likelihood functions for symmetric hyperbolic distributions. Then, we estimate these parameters by maximizing the log-likelihood functions. Using these MLE parameters for the generalized hyperbolic distributions, we obtain the minimum variance hedge ratio and the optimal Sharp hedge ratio. Also based on the MLE parameters and the numerical method, we can calculate the minimum generalized semivariance hedge ratio.

Keywords : Optimal hedge ratio, Generalized hyperbolic distribution, Martingale property, Minimum variance hedge ratio, Minimum generalized semi-invariance, Maximum Sharp measure, Joint-normality assumption, Hedging effectiveness

1.1.94 Chapter 95: Instrument Variable Approach to Correct for Endogeneity in Finance

This chapter reviews the instrumental variables (IV) approach to endogeneity from the point of view of a finance researcher who is implementing instrumental variable methods in empirical studies. This chapter is organized into two parts. Part I discusses the general procedure of the instrumental variable approach, including Two-Stage Least Square (2SLS) and Generalized Method of Moments (GMM), the related diagnostic statistics for assessing the validity of instruments, which are important but not used very often in finance applications, and some recent advances in econometrics research on weak instruments. Part II surveys corporate finance applications of instrumental variables. We found that the instrumental variables used in finance studies are usually chosen arbitrarily, and very few diagnostic statistics are performed to assess the adequacy of IV estimation. The resulting IV estimates thus are questionable.

Keywords : Endogeneity, OLS, Instrumental variable (IV) estimation, Simultaneous equations, 2SLS, GMM, Overidentifying restrictions, Exogeneity test, Weak instruments, Anderson-Rubin statistic, Empirical corporate finance

1.1.95 Chapter 96: Application of Poisson Mixtures in the Estimation of Probability of Informed Trading

This research first discusses the evolution of probability of informed trading in the finance literature. Motivated by asymmetric effects, e.g., return and trading volume in up and down markets, this study modifies a mixture of the Poisson distribution model by different arrival rates of informed buys and sells to measure the probability of informed trading proposed by Easley et al. (1996). By applying the expectation–maximization (EM) algorithm to estimate the parameters of the model, we derive a set of equations for maximum likelihood estimation and these equations are encoded in a SAS Macro utilizing SAS/IML for implementation of the methodology.

Keywords : Probability of informed trading ( PIN ), Expectation–maximization (EM) algorithm, A mixture of Poisson distribution, Asset-pricing returns, Order imbalance, Information asymmetry, Bid-ask spreads, Market microstructure, Trade direction, Errors in variables

1.1.96 Chapter 97: CEO Stock Options and Analysts’ Forecast Accuracy and Bias

This chapter uses ordinary least squares estimation to investigate the relations between CEO stock options and analysts’ earnings forecast accuracy and bias. Our OLS models relate forecast accuracy and forecast bias (the dependent variables) to CEO stock options (the independent variable) and controls for earnings characteristics, firm characteristics, and forecast characteristics. In addition, the models include controls for industry and year. We use four measures of options: new options, existing exercisable options, existing unexercisable options, and total options (sum of the previous three), all scaled by total number of shares outstanding, and estimate two models for each dependent variable, one including total options and the other including new options, existing exercisable options, and existing unexercisable options. We also use both contemporaneous as well as lagged values of options in our main tests.

Keywords : CEO stock options, Analysts’ forecast accuracy, Analysts’ forecast bias, CEO compensation, Agency costs, Investment risk taking, Effort allocation, Opportunistic earnings management, Opportunistic disclosure management, Forecasting complexity

1.1.97 Chapter 98: Option Pricing and Hedging Performance Under Stochastic Volatility and Stochastic Interest Rates

This chapter fills this gap by first developing an implementable option model in closed form that admits both stochastic volatility and stochastic interest rates and that is parsimonious in the number of parameters. Based on the model, both delta-neutral and single-instrument minimum variance hedging strategies are derived analytically. Using S&P 500 option prices, we then compare the pricing and hedging performance of this model with that of three existing ones that, respectively, allow for (i) constant volatility and constant interest rates (the Black-Scholes), (ii) constant volatility but stochastic interest rates, and (iii) stochastic volatility but constant interest rates. Overall, incorporating stochastic volatility and stochastic interest rates produces the best performance in pricing and hedging, with the remaining pricing and hedging errors no longer systematically related to contract features. The second performer in the horse-race is the stochastic volatility model, followed by the stochastic interest rates model and then by the Black-Scholes.

Keywords : Stock option pricing, Stochastic volatility, Stochastic interest rates, Hedge ratios, Hedging, Pricing performance, and Hedging performance

1.1.98 Chapter 99: The Le Châtelier Principle of the Capital Market Equilibrium

This chapter purports to provide a theoretical underpinning for the problem of the Investment Company Act. The theory of the Le Chatelier Principle is well known in thermodynamics: The system tends to adjust itself to a new equilibrium as far as possible. In capital market equilibrium, added constraints on portfolio investment in each stock can lead to inefficiency manifested in the right-shifting efficiency frontier. According to the empirical study, the potential loss can amount to millions of dollars coupled with a higher risk-free rate and greater transaction and information costs.

Keywords : Markowitz model, Efficient frontiers, With constraints, Without constraints, Le Chatelier principle, Thermodynamics, Capital market equilibrium, Diversified mutual funds, Quadratic programming, Investment company act

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Lee, CF., Lee, J.C. (2015). Introduction to Financial Econometrics and Statistics. In: Lee, CF., Lee, J. (eds) Handbook of Financial Econometrics and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7750-1_1

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DOI : https://doi.org/10.1007/978-1-4614-7750-1_1

Published : 09 August 2014

Publisher Name : Springer, New York, NY

Print ISBN : 978-1-4614-7749-5

Online ISBN : 978-1-4614-7750-1

eBook Packages : Business and Economics

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