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The importance of the financial derivatives markets to economic development in the world’s four major economies.

Graphical Abstract

1. Introduction

2. literature review, 3. overview on the derivatives market in the ciju, 4. methodology, 4.1. model specification, 4.2. unit-root tests, 4.3. cointegration test, 4.4. granger-causality test, 5. empirical results, 5.1. unit root tests and cointegration tests, 5.2. effect of derivatives market development on economic growth and volatility, 5.3. granger causality effect of derivatives market development on economic growth and volatility, 6. concluding remarks, author contributions, acknowledgments, conflicts of interest.

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Share and Cite

Vo, D.H.; Huynh, S.V.; Vo, A.T.; Ha, D.T.-T. The Importance of the Financial Derivatives Markets to Economic Development in the World’s Four Major Economies. J. Risk Financial Manag. 2019 , 12 , 35. https://doi.org/10.3390/jrfm12010035

Vo DH, Huynh SV, Vo AT, Ha DT-T. The Importance of the Financial Derivatives Markets to Economic Development in the World’s Four Major Economies. Journal of Risk and Financial Management . 2019; 12(1):35. https://doi.org/10.3390/jrfm12010035

Vo, Duc Hong, Son Van Huynh, Anh The Vo, and Dao Thi-Thieu Ha. 2019. "The Importance of the Financial Derivatives Markets to Economic Development in the World’s Four Major Economies" Journal of Risk and Financial Management 12, no. 1: 35. https://doi.org/10.3390/jrfm12010035

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A Study on the Derivatives Market in India

9 Pages Posted: 16 Jan 2020

Toopalli Sirisha

Sri Venkateswara College of Engineering

Dr. Nalla Bala Kalyan

Sri Venkateswara College of Engineering (SVCE)

Date Written: December 27, 2019

Derivatives market a significant role to play in a country's economic development. The study's objective is to investigate the effect on the underlying market volatility of financial derivatives (futures and options). Currently, financial derivatives have become increasingly popular and utmost frequently used in the world of finance. This has grown with an extraordinary speed all over the world that now it is called as the derivatives revolution. In India, the emergence and growth of the derivatives market is relatively more derivative. This article aims to study futures and options by considering a company derivative from Indian stock market. This paper aims at suggesting the best possible ways to investors to gain more profits in derivative markets.

Keywords: Financial Market, Derivatives, Futures, Options, India

Suggested Citation: Suggested Citation

Sirisha Toopalli (Contact Author)

Sri venkateswara college of engineering ( email ).

Karakambadi Road Opp.LIC Training Centre TIRUPATI, AR Andhra Pradesh 517507 India 7845130280 (Phone) 0877-2285992 (Fax)

HOME PAGE: http://https://scholar.google.co.in/citations?user=4wfWCU0AAAAJ&hl=en

Nalla Bala Kalyan

Sri venkateswara college of engineering (svce) ( email ).

Karakambadi Road Opp.LIC Training Centre Tirupati-517507, Chittoor District, AP India 9573068980 (Phone) 08772285992 (Fax)

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27 Research Issues in Commodities and Commodity Derivatives

• Published: March 2018
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Commodity derivatives allow hedgers to smooth wealth across different states of the world and provide a direct link between financial markets and the real economy. This chapter describes recent research related to core concepts for these products: hedging behavior, the provision of liquidity in commodity markets, and the “financialization” debate related to the recent inflow of institutional investments into commodities. Intermediation activity in these markets has evolved in recent years. Modern commodity markets now incorporate extensive intermediation by swap dealers to facilitate over-the-counter swap dealing activity and execute the associated hedging activity linking swap and futures markets. The emergence of highly computerized, automated trading and the trend toward liquidity provision by commercial firms are intermediation issues that require better understanding. The chapter also provides insights into the types of topics faced by researchers who consider policy-related issues.

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India proposes tighter rules for derivatives trading on individual stocks

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Reporting by Jayshree P Upadhyay; Editing by David Holmes

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World leaders join Ukraine summit in test of Kyiv's peace push

President Biden skipped the summit, China shunned it and Moscow dismissed it as a waste of time.

SEBI plans tighter norms for derivatives

MUMBAI: Amid massive spike in derivatives volumes and increasing participation of retail investors in this segment despite over 91% of the heavily losing money from such trades, market watchdog Sebi has proposed tighter rules for derivatives contracts on individual stocks by ensuring sufficient liquidity and trading interest from market participants, something currently required only for contracts on indices.

In a discussion paper the regulator has proposed that for a stock to be considered for futures and options (F&O) trading, it need to have traded for 75% of the trading days.

Mooting tighter rules on trading in individual stock derivatives, the Sebi says such liquidity rules are needed to avert risks of market manipulation after recent explosive growth particularly in options trading. The paper comes amid reports that Sebi and other financial regulators were planning to form a panel to assess stability risks emerging from a surge in derivatives markets.

F&O in general and options trading in particular has soared in the past five years, fueled mainly by retail investors who by Sebi’s own data have been losing from this exposure. The growth in this segment has been such that the notional value of index options traded over doubled in FY24 to $907.09 trillion from the previous year before, as per NSE, which controls over 95% of the domestic derivatives volume which also makes it the world’s largest in this segment.

Of the 108 billion options contracts traded worldwide in 2023, as much as 78% were on domestic exchanges, as per the data from the Futures Industry Association. Retail investors make up 35% of derivative trading in the country.

An IIFL note warned that 25 of the 182 stocks on which F&O contracts are traded will ineligible for trading if the regulator’s proposals are implemented.

Sebi further said derivatives contracts on individual stocks should have sufficient liquidity and trading interest from market participants, to the extend that a stock to be included

in the F&O segment should have been traded for at least 75 percent of the trading days, Sebi said without specifying over what period.

Also, Sebi wants 15% of active derivatives traders should have traded the stock; average premium daily turnover should be Rs 150 crore; average daily turnover must be between Rs 500 crore and Rs 1,500 crore; and the maximum number of open F&O contracts permitted for the underlying stock must be Rs ,250-1,750 crore, Sebi said, again without specifying any time periods.

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Staff Working Papers Working Paper 24-03: The Lock-In Effect of Rising Mortgage Rates

​​​​​​Ross M. Batzer, Jonah R. Coste, William M. Doerner, and Michael J. Seiler

​​Ab​stract:

People can be “locked-in” or constrained in their ability to make appropriate financial changes, such as being unable to move homes, change jobs, sell stocks, rebalance portfolios, shift financial accounts, adjust insurance policies, transfer investment profits, or inherit wealth. These frictions—whether institutional, legislative, personal, or market-driven—are often overlooked. Residential real estate exemplifies this challenge with its physical immobility, high transaction costs, and concentrated wealth. In the United States, nearly all 50 million active mortgages have fixed rates, and most have interest rates far below prevailing market rates, creating a disincentive to sell. This paper finds that for every percentage point that market mortgage rates exceed the origination interest rate, the probability of sale is decreased by 18.1%. This mortgage rate lock-in led to a 57% reduction in home sales with fixed-rate mortgages in 2023Q4 and prevented 1.33 million sales between 2022Q2 and 2023Q4. The supply reduction increased home prices by 5.7%, outweighing the direct impact of elevated rates, which decreased prices by 3.3%. These findings underscore how mortgage rate lock-in restricts mobility, results in people not living in homes they would prefer, inflates prices, and worsens affordability. Certain borrower groups with lower wealth accumulation are less able to strategically time their sales, worsening inequality.​

​Mortgage lock-in data are available below in two formats at the bottom of this webpage. The first file offers a data supplement that could be used to recreate figures shown in the working paper. The second file offers additional developmental data aggregates produced from estimations in the working paper. Both files are subject to change with working paper revisions. Our  FA​Qs  address common questions about the datasets. Please cite this working paper when using either dataset.​

• Supplemental data​​ for figures  (1 MB)
• ​​​ Developmental data aggregates ​ (45 MB)​

The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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