Unveiling information asymmetry: analyzing spot-future price relations during cash and physical delivery settlements

The Indian financial sector underwent a significant transformation with economic liberalization in 1991. In 1994, the trading system evolved from the traditional shouting and gesticulation pattern to an electronic, anonymous, order-driven, and screen-based system. Exchanges implemented satellite communication technology to connect 200 major cities across India, enabling active and real-time participation from traders. This technological advancement facilitated nearly 100 percent electronic order-matching in India’s financial markets.

Derivative trading, along with its settlement and clearing procedures, has undergone significant changes in India over time. The history of derivative trading in India can be traced back to the LC Gupta Committee formation. On November 18, 1996, the Securities and Exchange Board of India (SEBI) appointed Dr. L.C. Gupta to develop an appropriate regulatory framework for derivative trading in India. The committee submitted its report in March 1998. On May 11, 1998, SEBI accepted the committee’s recommendations and approved the phased introduction of derivative trading in India, beginning with stock index futures.

The “Derivative Market Review Committee,” constituted by SEBI under the chairmanship of Prof. M. Rammohan Rao, was tasked with reviewing the development of the derivatives market and recommending future courses of action. The committee recommended broadening the range of products, including options contracts with longer tenures, creating a volatility index with futures and options contracts based on it, exchange-traded currency contracts, and mini contracts on equity indices. Additionally, the committee suggested revising the eligibility criteria for stocks in futures and options and increasing the position limits.

Following the recommendations of the Dr. L.C. Gupta Committee and the Prof. M. Rammohan Rao Committee, trading in financial derivatives in India commenced in a phased manner. The Bombay Stock Exchange (BSE) launched exchange-traded derivatives on June 9, 2000. The National Stock Exchange (NSE) began trading in the derivatives segment by introducing index futures (Nifty 50) on June 12, 2000. Subsequently, on June 1, 2001, BSE started trading in index options on the Sensex, followed by stock options for 31 stocks on July 9, 2001. Similarly, NSE introduced trading in index options on June 4, 2001, and was the first exchange to offer options on single stocks from July 2, 2001, as well as single-stock futures from November 9, 2001. BSE began trading in stock futures on individual securities starting November 9, 2002.

The success of a futures and options contract is closely tied to its contract specifications. In light of prevailing economic and business conditions, exchanges must regularly review and adjust contract specifications to ensure that futures market operations remain efficient and effective. These contract specifications outline the final settlement process, which can either be conducted through physical delivery or cash settlement. Lien (1990) compared conventional cash-settled contract specifications with optimal contract specifications. The study found that both trading volume and the utility for hedgers were lower in conventional cash-settled contracts compared to optimal contract specifications.

Ensuring the recommendations of the Dr. L.C. Gupta Committee and discussions by the Secondary Market Advisory Committee, SEBI transitioned from cash settlement to a physical delivery system in Indian Individual Share F&O contracts in a phased manner as outlined in the December 2018 circular^{Footnote 1}. This change was implemented to enhance market integrity and ensure better convergence between the cash and derivative markets.

In a cash settlement system, the underlying asset in the contract is not physically transferred. Instead, the settlement of the derivative position is based on the spot and strike price of the underlying asset, and the price difference is paid to the party benefiting from the contract. In contrast, in a physical delivery system, the underlying asset is delivered at the contract’s maturity.

The effectiveness of both settlement modes has been widely debated. Garbade and Silber (1983a) emphasized the significance of cash settlement and its impact on futures contracts, noting that cash-settled futures contracts can facilitate price convergence between the futures market and its underlying cash market, thereby enhancing market efficiency.

This study examines the impact of settlement changes on the price discovery function of Indian individual share futures (ISF) contracts. Specifically, it investigates whether the transition from cash settlement to physical delivery improves the exchange of information flow between the spot and futures markets, thereby influencing the efficiency and performance of the derivatives market. The study employs Geweke’s (1982) measures of information feedback to assess the information flow between the spot and futures markets. The findings indicate that the transition to a physical delivery system led to a reduction in the information flow between the spot and futures markets for a significant number of stocks. These results carry important implications for investors, policymakers, and regulators in India. Regulators revised delivery specifications intending to mitigate excess volatility in the futures market.

The structure of the paper is as follows: section “Review of literature” presents a comprehensive literature review, highlighting research gaps and key findings from existing studies. Section “Data and methodology” elaborates on the research methodology, detailing Geweke’s (1982) measures of information feedback, the study period, and the sample size. Section “Empirical results” discusses the results and examines the practical implications of settlement transitions. Section “Conclusion” summarizes the study’s significant findings and limitations and provides directions for future research on settlement types and their impact on spot-futures market dynamics.

After introducing futures and options contracts, their specifications were scrutinized in a phased manner to make market operations more conducive. Typically, at the inception of a futures contract, futures prices tend to remain higher due to factors like time value and other associated costs, such as interest rates, dividend yields, storage, transportation, and insurance. The relationship governing the value of futures prices is determined by the cost-of-carry model, as outlined by Modest and Sundaresan (1983) and Cornell and French (1983). According to this model, the futures price is derived by adding the cost of carrying the underlying asset to its spot price.

In an efficient market, futures prices converge with their underlying cash market prices as the contracts reach expiration. This means that at the time of expiration, futures prices become equivalent to the spot prices. The key factor influencing this process of price convergence is the cost of carry, which diminishes over time and eventually becomes zero as the contract nears maturity.

Several studies have examined the cost of carry model, which posits that futures prices are derived by adding the cost of carrying (including factors like interest rates, storage, and insurance) to the spot price. However, researchers such as Cornell and French (1983), MacKinlay, Ramaswamy (1988), Yadav and Pope (1990), Neal (1996), Lee (2005), and Vipul (2005) have argued that the cost of carry model has limited practical relevance, as market realities often deviate from its theoretical assumptions.

Brailsford and Cusack (1997) employed three models—Adjusted Cost of Carry, Ramaswamy-Sundaresan, and Hemler-Longstaff—to investigate the pricing of equity futures. Their findings revealed the frequent occurrence of minor pricing errors in ISF contracts, a pattern consistently observed across all three models. This mispricing presents a small but persistent arbitrage opportunity within the model framework. The presence of such discrepancies between cash and futures prices creates risk-free arbitrage opportunities for investors and market participants. The debate on the mispricing of futures contracts was first initiated by Cox et al. (1981), highlighting its significance in market efficiency and arbitrage practices.

Similarly, Cornell and French (1983), Yadav and Pope (1990), Vipul (2005), and Lee (2005) argued that futures contracts tend to be underpriced, which can provide misleading signals about potential movements in the spot market. This mispricing could adversely affect the interests of traders by distorting market expectations. In contrast, studies by Cornell and Reinganum (1981), Diagler (1990), and Bae et al. (1998) did not identify any significant evidence of mispricing between spot and futures prices, suggesting that such discrepancies may not always occur under normal market conditions.

To enhance market integrity and address the evolving needs of futures contracts for better functionality, the SEBI transitioned the final settlement of derivatives from a cash settlement system to a physical delivery system. However, Garbade and Silber (1983b), Jones (1982), Manaster (1992), and Edward and Ma (1996) highlighted that cash settlement emerged as a viable alternative due to the increased risk of market manipulation and the high costs associated with physical delivery. Additionally, the absence of adequate liquidity in the underlying cash market and the lack of a robust Stock Lending and Borrowing mechanism further reinforced the preference for cash-settled derivatives contracts.

Kumar and Seppi (1992) highlighted the significant welfare implications of the cash settlement system, emphasizing its vulnerability to exploitation for price manipulation by traders. They also found that informed spot traders benefit from the cash settlement system by increasing spot liquidity. Manipulations in spot trading can inflate the settlement price, ultimately disadvantaging futures noise traders. Similarly, Jones (1982), Paul (1984), Kumar and Seppi (1992, 1994), Jarrow (1992), Pirrong (1993, 1995, 1997, 2001), and Mollgaard (1997) examined market manipulation in both cash settlement and physical delivery systems. Adil and Siddiqui (2019) analyzed the impact of cash settlement and physical delivery on various commodities in Islamic contracts, concluding that physical delivery is more reliable for rice, wheat, sugar, and gold futures contracts.

Market manipulation can be facilitated by the availability of information, particularly the futures price, which is shared among market participants. The efficiency of information flow, defined by the speed at which news circulates among investors, plays a crucial role in determining market behavior. Notably, markets tend to be more responsive to negative or confrontational news than to positive news (Dedi and Yavas, 2016). Several studies have examined the shift from one settlement system to another, exploring its impact on the informativeness and price discovery role of futures contracts. These studies suggest that the type of settlement system, whether cash settlement or physical delivery can significantly influence how market participants process and react to information, thereby affecting market efficiency and the potential for manipulation.

Bollen and Whaley (1999) investigated the impact of transitioning from cash settlement to physical delivery in the Australian single-stock futures market. Their study found that the adoption of physical delivery mechanisms led to a significant improvement in information flow within the futures market. Similarly, Chan and Lien (2001) observed that when the market shifted from physical delivery to cash settlement, the futures market played a pivotal role in providing valuable, real-time information to various participants in the spot market, particularly in the context of price discovery. The flow of information from the futures markets, especially regarding futures prices, influenced the behavior of cash market participants, contributing to feedback mechanisms that impacted the pricing dynamics of futures contracts.

Da Cunha Amarante et al. (2018) emphasized the importance of price information in the decision-making processes of market participants, particularly in the Brazilian physical beef cattle market, where such information is essential for price discovery. Consistent with these findings, an active futures market provides crucial information that aids in the price discovery function and fosters price convergence between spot and futures prices. This highlights the critical role of futures markets in ensuring efficient price formation and improving market transparency across both spot and futures markets.

Chan and Lien (2002) revisited alternative settlement specifications in their study, which found that the introduction of a cash settlement system in feeder cattle futures contracts significantly enhanced the price discovery function. This improvement benefited all market participants by providing more timely and accurate pricing information.

In contrast, Lien and Yang (2003) conducted a study on the transition of ISF contracts from cash settlement to physical delivery. Using Geweke’s (1982) feedback measures, they extensively analyzed the information flow between the futures and spot markets. The findings indicated that the shift to a physical delivery system led to an increase in both the significance and magnitude of information transmission from the spot market to the futures markets. This shift bolstered the informational function of the spot market, leading to better market efficiency and improved price discovery.

In contrast, Lien and Yang (2003) conducted a study on the transition of ISF contracts from cash settlement to physical delivery. Using Geweke’s (1982) feedback measures, they extensively analyzed the information flow between the futures and spot markets. The findings indicated that the shift to a physical delivery system led to an increase in both the significance and magnitude of information transmission from the spot market to the futures markets. This shift bolstered the informational function of the spot market, leading to better market efficiency and improved price discovery.

Building on these insights, the present study explores the impact of settlement changes on the price discovery function of Indian ISF contracts. It specifically investigates whether transitioning from cash settlement to physical delivery enhances the exchange of information between the spot and futures markets, thereby improving overall market efficiency and price convergence.

The present study investigates the impact of information flow between the spot and futures markets during cash settlement and physical delivery periods. As of December 1, 2019, the NSE of India listed 147 active futures and options contracts. For this study, 44 actively traded single-stock futures were selected for analysis. In India, futures contracts have a 3-month expiration cycle comprising far-month, middle-month, and near-month contracts. This study focuses on near-month contracts, as they are the most actively traded in the market. A continuous time series was constructed by rolling over to the first day of the next near-month futures contract upon expiration.

The study period spans from January 1, 2014 (the initial listing date of each ISF contract) to February 2020. Data points with missing values in either spot or futures prices on the same day were excluded, resulting in a final sample size ranging from 661 to 1522 observations per stock. The data cutoff in February 2020 was implemented to avoid the potential impact of COVID-19 on stock prices. Stock selection was based on data availability, with price series data sourced from the Bloomberg database (Table 1).

The empirical analysis employs the two-step Engle-Granger cointegration approach to examine the long-term relationship between the spot and futures markets. Furthermore, Geweke’s (1982) measures of information feedback are utilized to assess the bidirectional flow of information between the spot and futures markets.

Let p_st and p_ft denote the natural logarithms of the spot and futures prices at time t. The return series, the nominal return of spot and futures prices, is calculated as Δp_st = (p_st−p_{s, t−1)} and Δp_ft = (p_ft−p_{f, t−1)} respectively. The basis, that is, simple differences between the futures prices and their underlying cash prices at a given time, is calculated as B_t = s_t−f_t. At time t−1, B_t−1 serves as the error-correction factor.

Before conducting the time series analysis, a preliminary analysis of the mean, standard deviation, skewness, and kurtosis is performed for the study. The standard deviations in the descriptive statistics show that the standard deviation of 19 stocks (both spot and futures markets) increased rapidly, suggesting an increase in volatility for these stocks after switching from cash settlement to physical delivery. Whereas 23 stocks show a decrease in volatility, and for stocks SIEM and IGL, the spot market is more volatile during the physical delivery period. By contrast, the futures market is more volatile during the cash settlement period. A brief overview of this pattern of standard deviation for the selected 44 stocks after the settlement change to physical delivery is presented in Table 2. A similar standard deviation pattern also applies to kurtosis. The complete descriptive results are presented in Table 3.

Unit root and Engle-Granger cointegration test

The unit root test was performed for the price series and its first difference (return) series. The Augmented Dickey-Fuller (ADF) test was applied to the price and return series; the lag length was determined using Schwarz Bayesian Criteria (Schwarz, 1978) with no trend and constant in the model.

When the spot and futures returns are integrated in the same order. A cointegration approach is used to examine the long-term relationship between the spot and futures markets. A two-step Engle-Granger Cointegration (1987) method is used to construct a residual-based static regression to study the relationship between spot and futures markets. First, we run a simple regression model for the spot and futures prices. If the test residuals are stationary, then both variables are cointegrated.

Geweke (1982) measures of information flow

In the long run, the spot and futures prices are expected to co-move, reflecting their equilibrium relationship. However, deviations from this long-run equilibrium may indicate short-term mispricing between the spot and futures markets. Thus, the spot and futures market will move in a stochastic trend according to the cost-of-carry theory. This implies that the spot and futures prices are cointegrated. To represent the cointegration between spot and futures prices the model applies a bivariate vector error-correction model.

Where k denotes the number of lags used in the model. The basis at time t−1, B_t−1 serves as an error-correction factor term. The residuals, denoted as ε_st and ε_ft exhibit are serially uncorrelated and can simultaneously correlate with each other.

The variance-covariance matrix of residuals ε_st and ε_ft can be written as,

Equations (1) and (2) would simplify in the absence of an intertemporal relationship between spot and futures prices; that is, if β_sj = α_ft = 0 for i, j = 1,….,k.

In Eqs. (3) and (4), the residuals are distributed autonomously and identically, with zero mean and variances of var(η_st) = σ²_us and var(η_ft) = σ²_uf, respectively.

The information flow from the spot to the futures market and from futures to the spot market using Geweke (1982) can be measured as,

Similarly, the instantaneous linear feedback measures between the spot and futures markets can be measured by;

And finally, the linear correlation between two markets can be quantified as

Or

Where ∣Σ∣denotes the determinant operator. In Geweke (1982), Eqs. (1) and (2)use a seemingly related regression model to estimate the variance/covariance matrix from the system equation. The ordinary least squares method is used in Eqs. (3) and (4) to estimate the variances of each individual stock. Using this, we calculate the measures of information flow between spot and futures prices. If the residuals from Eqs. (1) to (4) are independent and identically distributed. Using these models, the null hypothesis is set and there is no information feedback between the spot and futures markets. i.e., ({H0:F}_{sto f}=0,n{hat{F}}_{sto f}mathop{sim }limits^{a}{chi }^{2}(k)) and ({{rm{H}}}_{0}:{F}_{fto s}=0,n{F}_{fto s}mathop{sim }limits^{a}{chi }^{2}(k)), where n is the sample size and k is the number of lags used in the model. If the model F_(s→f) is statistically significant and F_(f→s) where not, we conclude that there is an information flow from the spot to the futures market and not from the futures market to the spot market, and the spot prices lead to futures prices. Similarly, if F_(f→s) is statistically significant and F_(s→f) is not, then it concludes that there is an information flow from the futures to the spot market and not from the spot to futures market, and the futures prices will lead the spot prices. The significance and magnitude of F_(f→s) describe the effectiveness the futures market for the price discovery function, and a larger F_(f→s) implies that futures prices serve their price discovery function more effectively.

The instantaneous linear feedback measures between the spot markets and futures can be measured by F_(f↔s), the null hypothesis of ({{rm{H}}}_{0}:{F}_{sleftrightarrow f}=0,n{hat{F}}_{sleftrightarrow f}mathop{sim }limits^{a}{chi }^{2}(1)). A larger F_(s↔f) indicates a stronger contemptuous relationship between the spot and futures markets through co-movements when a shock occurs in the market. Finally, F_(s.f) shows the linear dependence between the spot and futures markets and its null hypothesis is set as ({{rm{H}}}_{0}:{F}_{s.f}=0,n{F}_{s.f}mathop{sim }limits^{a}{chi }^{2}left(2k+1right).) The study used the gwke82 command developed by Dicle and Levendis (2013) to estimate the model.

Unit root and Engle-Granger cointegration

The unit root test was conducted on the price series and its first difference (return) series. The ADF test was applied to both the price and return series, with the lag length determined using the Schwarz Bayesian Criterion (Schwarz, 1978). The model specification excluded both trend and constant terms. The results indicate that while the spot and futures price series are non-stationary, the return series are stationary. Once the spot and futures returns are integrated in the same order, it is recommended to examine their long-run relationship.

A two-step Engle-Granger cointegration model is employed for this purpose. First, a simple regression model is applied to estimate the residuals, and then the stationarity of these residuals is tested. If the residuals are stationary, it indicates that the spot and futures markets are cointegrated. The results reveal cointegration between the variables during the cash settlement, physical delivery, and the entire sample periods. The unit root and cointegration test findings are presented in Table 4.

Geweke’s model 1982 measures of information feedback

The ensuing analysis employs Geweke’s (1982) model to assess the information flow and interdependence between Indian ISF contracts and their underlying stock markets. Additionally, it seeks to investigate whether the effectiveness of the price discovery function of futures prices has improved following the transition from cash settlement to physical delivery. For the model, Eqs. (1) and (2) are used to obtain the variance/covariance matrix of the residuals, and Eqs. (3) and (4) are used to measure the variance of the residuals of individual stocks. Following the computation of the variance/covariance matrix and variance of the residuals, statistical measures of information flows are determined. The number of observations used coefficient value, significance value, and degree of freedom in cash settlement and physical delivery period are reported in Tables 5 and 6 (in the appendix).

The F_f→s values capture the flow of information from the futures market to the spot market, while F_{s→ f} reflects the converse. The F_s↔f values gauge the instantaneous information exchange between the two markets. Gewekee showed that the measures above are asymptotically distributed as an F distribution. When the F_f→s and F_s→f values are multiplied by their own observations, the resultant value is asymptotically distributed as a chi-square. The significance of information flow, whether from the spot market to futures or from futures to spot, hinges on whether the computed value of nF_s→f or nF_f→s surpasses the critical value corresponding to the designated significance level.

Table 5 presents the measures of information flow during the cash settlement system. Among the 44 stocks analyzed, the measures of nF_s→f are significant for 22 stocks, indicating information feedback from the cash market to the futures market. Specifically, stocks such as TPW, APTY, FB, GMRI, NACL, and NJCC are statistically significant at the 1% level, while SIEM, TRCL, IGL, and MGFL are significant at the 5% level. The remaining 12 stocks are significant at the 10% level. Conversely, 22 stocks do not exceed the critical value at any significance level, suggesting no information flow from the spot to the futures market during the cash settlement period.

For the flow of information from the futures to the spot market (F_f→s), statistically significance is observed in 20 stocks. Among these, SIEM, BATA, GMRI, and NACL, are significant at the 1% level. Additionally, SRF, TPW, BIL, MGFL, NJCC, and SAIL are statistically significant at the 5% level, and the remaining 10 stocks are significant at the 10% cent level. In contrast, 24 does not exhibit any information flow from the futures to the spot market.

Furthermore, the coefficients of F_s→f is significant for 9 out of 44 stocks, and F_f→s is not, indicating a unidirectional information flow from the spot market to the futures market for these stocks. This suggests that spot prices likely lead futures prices in these cases. Similarly, for 7 out of 44 stocks, the coefficient F_f→s is significant, whereas F_s→f is not, implying a unidirectional information flow from the futures market to the spot market for these stocks. This indicates potential futures price leadership over spot prices in these cases.

During the physical delivery period, the information flow from the spot market to the futures market is significant for 18 out of 44 stocks. Among these, six stocks—JUST, NITEC, EQUIT, JSP, MGFL, and TTCH are significant at a 1% level, indicating a strong information flow in this direction. Additionally, nine stocks, including ADANI, PVRL, TPW, BIL, ESC, FB, GNP, MTCL, and NACL, are significant at the 5 % level, while three stocks, SIEM, GMRI, and MAXF, show significance at the 10 % level. However, the remaining 26 stocks do not exhibit statistically significant information flow in this direction.

Conversely, the information feedback from the futures market to the spot market is significant for 17 stocks. Among these, JUST and MGFL are significant at a 1% level, while six stocks—PVRL, SIEM, ESC, JSP, NACL, and TTCH, show significance at the 5% level. BRGR, NITEC, TRCL, BATA, EQUIT, FB, IGL, MAXF, and UJJIV are significant at the 10% level. However, a larger group of 27 stocks do not exhibit statistically significant information flow from futures to spot, indicating no discernable influence in these cases.

In addition, the model F_s→f is significant for six out of 44 stocks, and F_f→s is not; thus, it is said that there is information flow from spot to futures and not from futures to spot market for these six stocks. Additionally, the spot prices lead to the futures prices. Similarly, the model F_f→s is significant, and F_s→f is not for five out of 44 stocks. It is concluded that there is an information flow from futures to spot markets for the five stocks but not from spot to futures market; hence, the futures price leads to the spot price for the five stocks.

The observations indicate that the transition to the physical delivery system resulted in a decline in the efficiency of information transmission between the spot and futures markets for a greater number of stocks. The results reveal that the shift from cash settlement to the physical delivery system reduced the information flow in both directions, i.e., from spot to futures markets and futures to spot markets. These findings suggest a weakening of the market’s price discovery function following the transition to the physical delivery system.

The instantaneous feedback nF_s↔f, captures the bidirectional information flow between the spot and futures markets. During both the cash settlement and physical delivery periods, all 44 stocks are statistically significant at a 1% level, confirming bidirectional information flow between the two markets. However, the results indicate that F_s↔f values increased for 12 stocks following the transition to the physical delivery system, whereas for 32 stocks, the F_s↔f value declined. The analysis suggests that adopting the physical delivery system for ISF contracts reduced the efficiency of information flow between the spot and futures market for most stocks.

The total feedback measure, F_s,f, is significant at the 1% level in the cash settlement and the physical delivery systems. Following the transition to the physical delivery system, the value F_s,f increased for 11 stocks, while it decreased for 33 stocks. This indicates that the information flow between the spot and futures markets declined for a majority of stocks during the physical delivery period. Consequently, the study concludes that the shift to physical delivery results in poorer market performance.

This research employed Geweke’s (1982) measures to examine the information feedback and interdependence between Indian ISF contracts and their underlying stocks during cash and physical settlement periods. During the cash settlement period, the information flow from the spot to the futures markets (F_s→f) was significant for 22 out of 44 stocks, while the remaining 22 stocks showed no significant information flow in this direction. Conversely, information flow from the futures markets to spot markets (F_f→s) was statistically significant for 20 out of 44 stocks, with the other 24 stocks exhibiting no significant flow in this direction.

During the physical delivery period, the information flow from the spot market to the futures market (F_s→f) was significant for 18 of 44 stocks, while the remaining 26 stocks did not demonstrate statistical significance. Similarly, information feedback from the futures market to the spot market (F_f→s) was significant for 17 stocks, whereas the other 27 stocks failed to exceed the critical value at any conventional significance level.

The bidirectional information flow between spot and futures markets, which signifies instantaneous information exchange, is confirmed by the observed instantaneous information flow between them. The findings reveal that coefficient values of F_s↔f decreased for 32 out of 44 stocks following the transition to the physical delivery system. This suggests that implementing the physical delivery system in ISF contracts reduced the information flow between the spot and futures markets for many stocks.

The coefficient values of total feedback measures (F_s,f) remain significant in the cash settlement and physical delivery systems at the 1% significance level. However, for 33 out of 44 stocks, the coefficient values decreased after the settlement change, indicating a decline in the information flow between the spot and futures markets for a majority of stocks during physical delivery. Following the transition to the physical delivery system, both spot and futures markets exhibited increased segmentation, leading to poorer market performance in Indian ISF contracts.

The findings from the study provide valuable insights into the Indian derivatives market and will be useful for policymakers, regulators, and exchanges. However, the study has certain limitations. The study period is limited to February 2020, owing to the disruptive effects of the COVID-19 pandemic. Additionally, the study focuses only on the equity futures segment, and future research could be extended to the options market and the commodity market to gain a more comprehensive understanding.