Journal of Futures Markets最新文献

In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in-sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out-of-sample analysis with various loss functions, we find that fossil energy return spillovers significantly outperform the benchmark and show better forecasting performance than the competing models using dimension reduction, variable selection, and combination approaches. The predictive ability of the spillovers also holds in long-term forecasting and does not derive from other carbon-related variables. It can bring substantial economic gains in the portfolio exercise within carbon futures. Finally, we provide economic explanations on the predictive ability of the fossil energy return spillover by the channels of the carbon emission uncertainty and the investor sentiment on the warming climate.

This study examines the left-digit bias of individual and institutional investors using the microstructural data set from a highly liquid index futures market. Both investor groups exhibit excess buying after the ask falls with a tens-digit decrement, whereas excess selling (buying) is observed only for institutions (individuals) after the bid rises with a tens-digit increment. Such excess buying is generally pronounced when price uncertainty is high. Institutional excess selling is evident when uncertainty is low and immediately after the market opens. While both investor groups focus on cognitive reference points, our findings imply that investors heterogeneously respond to the bias and that individuals experience investment losses as they trade on the bias.

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.