Journal of Futures Markets最新文献

This study investigates hedging performance with respect to different market structures for energy-related commodities, including West Texas Intermediate crude oil, Brent crude oil, Chinese crude oil, and Heating oil. Copula quantile regression functions and the generalized autoregressive conditionally heteroscedasticity model are combined to analyze the nonlinear impact of dependence and the heterogeneous impact of market structure changes on hedging performance. Results show that hedging performance presents nonlinearity and market structure changes have surprisingly strong heterogeneous effects on the quantile hedge ratio, where bearish and bullish have lower hedge ratios than normal markets, which is captured better by Clayton copula quantile regression. Additionally, the trend of hedging effectiveness over different market structures also shows an inverted U shape. After changing data frequency or the types of futures contracts, the conclusions remain the same. Our empirical findings imply that hedgers are supposed to adjust the hedging number of futures according to market structure changes to hedge price risk effectively.

This study investigated the volatility linkages between energy and agricultural futures, including possible causes for these comovements, such as external macroeconomic and financial shocks during low and high volatility regimes. A combination of Markov-switching regressions and quadrivariate VAR–DCC–GARCH and VAR–BEKK–GARCH modeling revealed that external shocks have an asymmetric effect on the relationship of these assets with higher cross-correlations reported during high volatility regimes. This comovement effect outweighs the substitution effect between energy and agricultural products. Furthermore, the quadrivariate VAR–BEKK–GARCH model provides strong evidence of a bidirectional price volatility spillover between the agricultural and energy markets during periods of high volatility. Overall, the results suggest that energy futures can be effectively used for hedging in a portfolio comprising agricultural futures (and vice versa), while a combination of macroeconomic and financial index futures can serve as an effective hedging tool in investment portfolios comprising both energy and agricultural commodities.

This study empirically tests whether price violations, as defined by Bakshi, Cao, and Chen (2000), show different patterns in response to market shocks. Specifically, we analyze the Chinese options market during a period covering a stock market crash and a series of trading restrictions in the Chinese derivatives markets. Our results confirm the significant changes of the defined violations in the face of unexpected shocks, and more importantly, we interpret such variations from the perspective of information spillovers. Our findings suggest that the stock market crash prompts informed traders in the Chinese options market to frequently adjust their positions on put options, exacerbating the misunderstandings and overreactions to new information. Further, the regulatory shock in the derivatives markets diminishes the efficiency of information incorporation for both options and spot markets but does not affect the dominance of the Chinese options market in price discovery.

The anomalous negative relationship between left-tail risk measures and future returns has recently attracted the attention of finance researchers. We examine the role of the derivatives market in attenuating left-tail risk anomaly in India, where derivatives trade only for a subset of stocks. We find that the negative association between left-tail risk measure and future return is absent only in stocks having derivatives, indicating that derivatives trading hastens the diffusion of negative information into the stock prices. We find evidence that the information generation role of derivatives markets plays a primary role compared to investor inattention and limits to arbitrage.

We extract implied price densities from wheat options and futures prices during the first 17 months of the Ukrainian war. Changing differences between short- and long-term densities indicate that market expectations about the dynamics of the underlying changed over time. Before the signing of the Black Sea Grain Initiative, wheat derivatives prices showed predictive power for the further development of the conflict, and implied volatilities from wheat options were highly correlated with geopolitical risk (GPR). Afterwards, wheat prices lost their predictive power for the conflict, but instead reflected the market's opinion regarding the viability of the Black Sea Grain Initiative. By that time, correlations between wheat price risk and GPR dropped sharply.

Derivatives are increasingly used by managers not only to hedge risks but also to pursue nonhedging activities for fulfilling opportunistic incentives. The Statement of Financial Accounting Standards No. 161 (SFAS 161) requires firms to disclose their objectives and strategies for using derivatives. Using the adoption of this standard, we examine whether and how derivative disclosures influence managerial opportunistic behavior. We employ insider trades and stock price crash risk to capture managerial opportunism. Applying a difference-in-differences research design with hand-collected data on derivative designations, we find that, after the implementation of SFAS 161, derivative users that comply with SFAS 161 experience a significantly greater decrease in both insider trades and stock price crash risk, compared with a matched control sample of nonderivative-users. We further provide evidence to suggest that SFAS 161 curbs managerial opportunism via reducing information asymmetry between corporate insiders and outside investors and enhancing the effectiveness of derivative hedging.

This paper studies the volatility dynamics of futures contracts on crude oil, natural gas, and gasoline. An appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models is estimated using daily prices for our futures contracts between 2005 and 2023. Moreover, to assess the impacts of COVID-19 and the Russia–Ukraine conflict on volatility, we analyze these two subsamples. Overall, we find that: (i) the Bayes factor shows that the SV model with $��t�$-distributed innovations outperforms the competing models; (ii) crude oil contracts with different expiry dates may require the introduction of leverage effects; (iii) the $��t�$-distributed innovations remain the appropriate model for the COVID-19 subsample, while jumps are needed in the conflict period; and (iv) other Bayesian criteria more appropriate to short-term predictive ability—such as the conditional and the observed-date deviance information criterion—suggest other rank order to model our futures contracts, despite the agreements for the best models.