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Purpose: This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023. Design/methodology/approach: The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing. Findings: The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes. Practical implications: These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market. Originality/value: The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants. Research highlights: This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes. We highlight how factors like volatility, futures volume, and open interest vary in their impact. The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing. We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities. [ABSTRACT FROM AUTHOR]

The article provides an in-depth analysis of trading strategies based on trading volumes (open interest) and their suitability for cryptocurrency trading. With the development of the cryptocurrency market, where volatility and liquidity differ significantly from traditional financial markets, there is a need to adapt classical approaches to new conditions. Analyzing trading volumes and tick data enables traders to assess market sentiment, identify entry and exit points, and predict potential market movements. A significant advantage of cryptocurrency exchanges compared to traditional ones is the availability of real-time access to all market data, including open interest. Cryptocurrency quotes and open interest data will form the basis of the input for the cross-platform automated cryptocurrency portfolio modeling system developed in the course of this research. A key role is assigned to filtering assets used by malicious actors for pump schemes to avoid potentially significant losses or even account liquidation. The value of this work lies in describing all models for filtering risky assets and incorporating the safest and most predictable cryptocurrencies into the portfolio based on their dynamics. These models form the foundation of a system that allows investment funds and other institutions investing in cryptocurrencies to adjust their portfolios in real-time (24/7), maximizing their safety. Additionally, an automated portfolio modeling system was created, which was tested on servers running different operating systems and proved to be portable and stable. [ABSTRACT FROM AUTHOR]

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility. [ABSTRACT FROM AUTHOR]

We examine the relation between excess corporate cash holdings and equity option market liquidity from January 3, 2005 to December 31, 2019. We show that the level of cash reserve in excess of what can be captured by firm characteristics significantly explains stock option liquidity. Trading volume and option open interest increase in companies with a higher magnitude of excess cash, whereas the bid–ask spreads of stock options decline in excess cash. Our findings confirm the theoretical prediction that excess cash improves option market liquidity as it reduces adverse selection problems caused by uncertainty in firm valuations. This relation remains more pronounced with put options, out‐of‐the‐money contracts, and short‐maturity contracts. In addition, excess cash has a stronger impact on option liquidity within firms that have a greater degree of informed trading and during high‐volatility periods in financial markets. Our results show that when uncertainty about firm prospects rises, excess cash becomes more valuable and affects option market liquidity. [ABSTRACT FROM AUTHOR]

This paper highlights the lessons drawn from the demise of the Brent Crude Oil futures contract that was traded on the Singapore Stock Exchange (SGX). We analyze the market microstructure of the contract prior to its failure—specifically, the number of trades, trading volume, open interest, bid–ask spread, and volatility. We find a steady decline in the mean volume, open interest, and number of trades as the contracts near their demise. The bid–ask spread of the contract also widens. Investigations of the mutual offset feature of the Brent Crude Oil futures contract between SGX and the International Commodity Exchange (ICE) provides evidence that trading volume, open interest, and the number of trades increase significantly during 4:00–5:45 PM local time when mutual offset is available. [ABSTRACT FROM AUTHOR]

Perpetual swaps are derivative contracts that allow traders to speculate on, or hedge, the price movements of cryptocurrencies. Unlike futures contracts, perpetual swaps have no settlement or expiration in the traditional sense. The funding rate acts as the mechanism that tethers the perpetual swap to its underlying with the help of arbitrageurs. Open interest, in the context of perpetual swaps and derivative contracts in general, refers to the total number of outstanding contracts at a given point in time. It is a critical metric in derivatives markets as it can provide insight into market activity, sentiment and overall liquidity. It also provides a way to estimate a lower bound on the collateral required for every cryptocurrency market on an exchange. This number, cumulated across all markets on the exchange in combination with proof of reserves, can be used to gauge whether the exchange in question operates with unsustainable levels of leverage, which could have solvency implications. We find that open interest in Bitcoin perpetual swaps is systematically misquoted by some of the largest derivatives exchanges; however, the degree varies, with some exchanges reporting open interest that is wholly implausible to others that seem to be delaying messages of forced trades, i.e., liquidations. We identify these incongruities by analyzing tick-by-tick data for two time periods in 2023 by connecting directly to seven of the most liquid cryptocurrency derivatives exchanges.

This study employs a bivariate EGARCH model to examine the Taiwan Futures Exchange's regular and after-hours trading, focusing on the critical aspects of spillover and expiration effects, as well as volatility clustering and asymmetry. The objective of this study is to observe the impact on the trading sessions in Taiwan by the influences of the European and American markets, focusing on the essential roles of the price discovery function and risk disclosure effectiveness of the regular hours trading. This research is imperative considering the increasing interconnectedness of global financial markets and the need for comprehensive risk assessment for investment strategies. It also examines the hedging behavior of after-hours traders, thereby aiming to contribute to pre-investment analysis by future investors. This examination is vital for understanding the dynamics of after-hours trading and its influence on market stability. Results indicate price continuity between both trading sessions, with regular trading often determining after-hours price ranges. Consequently, after-hours price changes can inform regular trading decisions. This finding highlights the importance of after-hours trading for shaping market expectations. Significant profit potential exists in after-hours trading open interest, which serves speculative and hedging purposes. While regular trading volatility influences after-hours trading, the reverse is not true. This suggests Taiwan market information poses a higher risk impact than European and American market data, emphasizing the unique position of the Taiwan market in the global financial ecosystem. After-hours trading volatility reflects the absorption of international market information and plays a crucial role in advance revelation of risks. This underscores the importance of after-hours trading in global risk management and strategy formulation. [ABSTRACT FROM AUTHOR]

This paper addresses the interpretability problem of non-parametric option pricing models by using the explainable artificial intelligence (XAI) approach. We study call options written on the S&P 500 stock market index across three market regimes: pre-COVID-19, COVID-19 market crash, and post-COVID-19 recovery. Our comparative option pricing exercise demonstrates the superiority of the random forest and extreme gradient boosting models for each market regime. We also show that the model's pricing accuracy has worsened from the pre-COVID-19 to the recovery period. Moneyness was the most important price determinants across the market regimes, while the implied volatility and time-to-maturity inputs contributed intermittently to a lesser extent. During the COVID-19 crash, open interest gained more economic importance due to the increased behavioral tendencies of traders consistent with market distress. [ABSTRACT FROM AUTHOR]

This study examines S&P 500 firms from 2008 to 2018 and uncovers a complex, non-linear relationship between the options market call-put ratio and investor sentiment that challenges assumptions of linearity. The link between options trading and underlying volatility appears disrupted during moderate pessimism but reemerges forcefully amid extreme negative sentiment. This suggests limits to arbitrage and information opacity enable sentiment transmission into prices. Notably, firm size moderates this relation, with larger firms partially insulated from sentiment-driven volatility extremes. Our findings challenge paradigms of simplistic sentimentvolatility connections, indicating the strength of such links depends on the sentiment regime, firm characteristics, and investor composition. Options market signals require carefully disentangling the prevalent investor mood and firm-specific informational frictions to interpret properly. Finally, this study enhances understanding of the intricate dynamics through which investor sentiment manifests in volatility dynamics. [ABSTRACT FROM AUTHOR]

Perpetual swaps are derivative contracts that allow traders to speculate on, or hedge, the price movements of cryptocurrencies. Unlike futures contracts, perpetual swaps have no settlement or expiration in the traditional sense. The funding rate acts as the mechanism that tethers the perpetual swap to its underlying with the help of arbitrageurs. Open interest, in the context of perpetual swaps and derivative contracts in general, refers to the total number of outstanding contracts at a given point in time. It is a critical metric in derivatives markets as it can provide insight into market activity, sentiment and overall liquidity. It also provides a way to estimate a lower bound on the collateral required for every cryptocurrency market on an exchange. This number, cumulated across all markets on the exchange in combination with proof of reserves, can be used to gauge whether the exchange in question operates with unsustainable levels of leverage, which could have solvency implications. We find that open interest in Bitcoin perpetual swaps is systematically misquoted by some of the largest derivatives exchanges; however, the degree varies, with some exchanges reporting open interest that is wholly implausible and others that seem to be delaying messages of forced trades, i.e., liquidations. We identify these incongruities by analyzing tick-by-tick data for two time periods in 2023 by connecting directly to seven of the most liquid cryptocurrency derivatives exchanges. 1. Introduction [ABSTRACT FROM AUTHOR]

We employ NASDAQ order level data to analyze intraday trading at option expirations and cross-market price pressure spillover. We observe more fleeting orders in optionable stocks on option expiration versus non-expiration days. The relation between NBBO proximity to strike prices and fleeting order direction, the relation between option Open Interest and fleeting order direction, as well as their placement outside NBBO suggest spoofing and price manipulation rather than a simple search for latent liquidity. We show that fleeting orders impact subsequent NBBO and increase likelihood of stock prices crossing option strike prices on option expiration days. [ABSTRACT FROM AUTHOR]

AbstractThis paper examines the relationship between trading activity and returns volatility in white maize futures listed on the South African Futures Exchange (SAFEX) and investigates the impact of speculative activity on volatility. Returns volatility is estimated using a GARCH (1,1) model. Trading activity changes are observed by computing two negatively correlated ratios from daily trading volume and open interest. The dynamic relationship between volatility and trading activity is explored over the period April 2000 to May 2022 using a vector autoregressive framework. The paper examines not only the Granger-causality between speculative and hedging ratios and volatility but also assesses their interactions through variance decomposition and impulse response functions. The first ratio, of volume to open interest, is used to capture speculative market activity; and the second, a ratio of the change in open interest to volume, is used to reflect the activity of hedgers. The results shed light on the effectiveness of targeting speculators for regulation in grain futures markets, while also contributing to the veracity of price limits in effectively moderating volatility.

Commonly trading restrictions have various impacts on asset prices and investors' speculative activities. This study uses the unique trading restrictions in China from 2015 to 2017 to study how it affects the price volatilities and speculative activities of China Securities Index 300 (CSI 300) futures. To capture the daily price volatilities precisely, we apply four different measures incorporate opening, high, low, and closing prices. Speculative and hedging activities are calculated by using volume and open interest data. We find interesting evidence that trading restrictions decrease the price volatilities and the trading activities of speculators. However, though the trading restrictions have been eased since 2017, the speculators are still unwilling to participate in futures market immediately. Overall, the trading restrictions affect the price volatilities and speculative activities and provide the protection for the majority of investors, however, it also hinders the price discovery function of the futures market. [ABSTRACT FROM AUTHOR]

The investment volume for commodity indices has increased rapidly over the past years. This financialization is intensively discussed in politics and science with mixed results because of several problems. We use a novel idea to measure the effect of the growing investment volume of index investors by looking at index rebalancing, in which only financial traders are forced to trade. Analyzing 289 rebalancing between 2006 and 2021 for the BCOM and the S&P GSCI, we observe significant results—with abnormal returns up to 14.1%—only for open interest and volume data. We cannot prove an effect on prices and, therefore, no effect of financialization on the real economy. [ABSTRACT FROM AUTHOR]

Sharp increases in systemically important crude oil prices have been a major cause of the recent surge in the inflation rate in the U.S. This paper investigates the extent to which the increase in oil prices can be attributed to excessive speculation in the oil futures market. Our analysis suggests that excessive speculation in the crude oil market has been responsible for 24%–48% of the increase in the WTI crude oil price during October 2020–June 2022. These estimates translate into an oil price increase of around $18-$36 per barrel and an increase in the U.S. PCE inflation rate by circa 0.75–1.5% points during the same period. We complement the analysis with an empirical investigation of the crude oil market, which shows that (speculative) long noncommercial open-interest positions in oil futures have increased considerably relative to short noncommercial positions. We further find that higher futures prices for crude oil "Granger-cause" oil spot prices, the futures prices of corn and soybeans and the fertilizer price. These econometric results show that oil speculators have to be held accountable for not just raising oil prices, but also driving up food commodity prices. We finally discuss measures to clamp down on excessive speculation in oil in order to eliminate its systemically adverse consequences for the U.S. economy. [ABSTRACT FROM AUTHOR]

We construct an agricultural policy uncertainty (APU) index from leading national and local newspapers in major agricultural states in the United States from January 1999 to September 2021. Our analysis shows that economic policy uncertainty Granger causes APU linearly in the short run, but the relationship is nonlinear and bidirectional in the long run. When economic policy uncertainty is considered, APU significantly reduces hedgers' net short positions and speculators' net long positions (except for corn hedgers) in the corn and soybean markets. APU also has a significant positive impact on the log returns of corn and soybean futures prices, consistent with the theory of storage where commodity returns and volatility are positively correlated. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of the "financialization" of commodity markets in terms of pricing. I explore whether the emergence of commodity index traders (CITs) affects weekly returns and turnover during the roll periods. I split the sample (1994–2017) into prefinancialization (1994–2003) and postfinancialization (2004–2017). I directly test whether the CIT market share (CIT/open interest) contributes to commodity returns and whether risk adjustments (based on momentum, basis, basis‐momentum, open interest, crowding, and average factors) alter liquidity and insurance premiums documented in Kang, Rouwenhorst, and Tang. I also examine how the financialization affects liquidity and insurance premiums. Finally, since previous results are obtained with Fama–MacBeth regressions, I use an alternative method to test how liquidity and insurance premiums determine commodity returns. [ABSTRACT FROM AUTHOR]

We investigate the effect of the Russian invasion of Ukraine on Black Sea Wheat Futures. Black Sea Wheat Futures are cointegrated with the Kansas City Wheat Futures, the global standard for wheat prices; however, the relationship between these two series significantly changes as a reaction to the main geopolitical events in the region. A significant drop in open interest after the invasion is also documented. The results of this study are relevant to many market participants, such as Ukrainian farmers and consumers in developing countries, including the World Food Program, which buys about forty percent of its wheat supplies from Ukraine. [ABSTRACT FROM AUTHOR]

To safeguard against foreign exchange risk due to high market volatility in today's globalized world, exporters use currency futures and options for smoothening the cash flows. This paper aims to analyze the performance of hedging foreign exchange risk using US dollar futures and options and their characteristics. The study uses data from 2016 to 2022. The results show that Indian exporters with a relatively high degree of risk aversion should prefer USD futures contracts with long-term maturities, low liquidity, and high open interest to hedge foreign exchange risk and generate maximum benefits in value enhancement. On the other hand, Indian exporters with a relatively low degree of risk aversion should use the protective put strategy with put options characteristics of long-term maturity, high liquidity, and out-of-the-money (in-the-money) put options if they have a strong conviction about the rising (declining) USD market. The results are robust as they are consistent for all individual years from 2016 to 2022 and for the overall period too. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between trading activity and returns volatility in white maize futures listed on the South African Futures Exchange (SAFEX) and investigates the impact of speculative activity on volatility. Returns volatility is estimated using a GARCH (1,1) model. Trading activity changes are observed by computing two negatively correlated ratios from daily trading volume and open interest. The dynamic relationship between volatility and trading activity is explored over the period April 2000 to May 2022 using a vector autoregressive framework. The paper examines not only the Granger-causality between speculative and hedging ratios and volatility but also assesses their interactions through variance decomposition and impulse response functions. The first ratio, of volume to open interest, is used to capture speculative market activity; and the second, a ratio of the change in open interest to volume, is used to reflect the activity of hedgers. The results shed light on the effectiveness of targeting speculators for regulation in grain futures markets, while also contributing to the veracity of price limits in effectively moderating volatility. [ABSTRACT FROM AUTHOR]

This study analyses the predictive power of six categories of investors who traded Nifty Index future contracts on the National Stock Exchange of India (NSE). Quality data were collected from The Securities and Exchange Board of India (SEBI). Investors' predictive potential was estimated by analysing the effect of open interest and volume traded on volatility for each category. The Holt–Winters (HW) exponential smoothing model successfully captured seasonality and provided a satisfactory analytical model for linear forecast. Nonlinearity was captured by Artificial Neural Networks (ANN). A resilient backpropagation algorithm with backtracking was used to determine the weights triggered by a logistic activation function for smoothing neurons. The multilayer perceptron network was further trained for time series data on volatility considering volume and open interest as input neurons. Predictive powers were considered best for the method with the least Root Mean Square Error (RMSE). The results suggest that nonlinearity in the data was well captured by the ANN as the RMSE for the ANN was smaller compared to the RMSE for the HW model. The RMSE using ANN was least for Foreign Institutional Investors (FIIs) that suggests that FIIs have better prediction potential compared to other investors. [ABSTRACT FROM AUTHOR]

This study performed comprehensive investigations of the complex interconnections between investor attention and cotton futures price volatility under different term structures. In this paper, in-sample analysis, out-of-sample forecast, influencing mechanisms, as well as nonlinear connections are fully explored using several linear model specifications. The results can be summarized as follows: first, investor attention is the Granger causality of the cotton futures volatility and shows significant linear impacts on cotton volatility; second, models incorporated with investor attention significantly improve the prediction accuracy of cotton volatility in the long term compared with the commonly used AR benchmark model; third, the influence of investor attention on cotton volatility may occur through open interest; and fourth, investor attention presents nonlinear impacts on cotton volatility as well. Overall, the results of this article can provide strong supporting evidence for the important roles of investor attention in asset pricing applications. [ABSTRACT FROM AUTHOR]