Journal of Futures Markets最新文献

Using a cutting-edge natural language processing method, this study constructs the China narrative policy index (CNPI) to capture policy content, drawing on 10 authoritative government reports and official media sources and incorporating expert input to enhance thematic precision and relevance. On this basis, the study employs a ripple-spreading network model to examine CNPI's impact on global commodity futures markets, revealing China's position in policy-driven spillovers across both temporal and spatial dimensions. Through case analyses of the supply-side reform and four sub-policy themes, we validate CNPI's effectiveness in capturing policy content and highlight China's influence as a global policy transmitter.

This study investigates the pricing of skewness risk in cross-sectional returns in the Chinese stock market, considering the substantial presence of retail investors and their potential lottery-related preferences. We decompose the total implied skewness, derived from the Shanghai Stock Exchange 50 exchange-traded fund options, into upper and lower components. Our findings reveal that the upper implied skewness carries a significantly negative price, whereas the lower implied skewness is positively but only weakly priced. The opposite predictability resolves the pricing puzzle associated with total implied skewness, which exhibits negligible cross-sectional predictability. The negative premium associated with upper skewness is attributed to retail investors' lottery preferences, as stocks exposed to higher upper skewness risk tend to perform well during right-tail market events. This behavioral interpretation is further supported by evidence showing that the negative premium on upper implied skewness is most pronounced during high-sentiment periods, even after controlling for standard risk factors.

This paper investigates the information value of U.S. Department of Agriculture (USDA) crop reports in terms of their impact on rational agents' expectations of future realized price volatility. While it is well known that uncertainty—proxied by options market implied volatility—is reduced in the wake of USDA reports, this is the first study to examine whether the information contained in USDA reports impacts market agents' ex ante expectations of realized volatility (RV). We use a Hamilton-type approach to reveal how August crop reports refine volatility expectations, and movements in RV in the post report period mirror these expectations. Importantly, in the wake of the USDA report releases, corn options partially reflect updates in volatility expectations. These updates are not instantaneous, highlighting potential short-term pricing misalignments over the first 2 days following the report release.

The shape of the pricing kernel has important implications for expected option returns. We shed light on the pricing kernel puzzle (i.e., mixed results regarding the shape of the pricing kernel) by examining S&P 500 index option returns and empirical pricing kernels across a wide range of expiration dates (from 1 month to 1 year). We document that at short (long) maturities, out-of-the-money call option returns are negative (positive) and decrease (increase) with the strike price. At short maturities, empirical pricing kernels predominantly exhibit a W-shape, while this pattern becomes less pronounced, evolving toward a monotonically decreasing curve at longer maturities. Our study suggests that the shape of pricing kernels and call option returns varies with option maturities, reflecting investors' heterogeneous beliefs about index returns across different time horizons.

The current international landscape is turbulent and unstable, with geopolitical risk having emerged as a significant threat. Focusing on the grain futures market, this paper builds different geopolitical risk measures by random matrix theory and constructs GJR-GARCH-MIDAS models to investigate the impact of geopolitical risk on grain market volatility. The findings indicate that rolling-window modeling performs better in describing the overall volatility of wheat, corn, soybean, and rice markets, and two-factor models generally exhibit stronger explanatory power in most cases. Short-term volatility demonstrates obvious volatility clustering and high volatility persistence, without significant asymmetry. Additionally, realized volatility of wheat, corn, and soybean significantly exacerbates their long-run volatility, while geopolitical risks of different dimensions show varying directions and degrees of effects in explaining long-term volatility of the four submarkets. This study offers valuable insights into grain market volatility and geopolitical risk, contributing to agricultural futures investment and global food security.

Utilizing a sample of 1899 M&A events from 1996 to 2020, we observe positive and significant abnormal trading volumes in the option market of target firms' rivals, particularly in out-the-money options. Our analysis further explores the underlying reasons for these patterns based on two major theories from existing literature: the acquisition probability theory, which suggests that rivals of target firms experience abnormal returns due to the increased likelihood of future acquisitions, and the collusion theory, which asserts that horizontal mergers lead to enhanced market power and, consequently, abnormal returns for rivals. Our findings support the acquisition probability theory.

This study examines the pricing of equity cross-sectoral crash (CSC) risk in the cross section of commodity futures returns. Theoretically, commodity futures with higher exposure to the CSC risk are expected to offer lower subsequent returns as they hedge against the CSC risk. We first construct a CSC risk measure by averaging the pairwise left-tail dependence across 17 sectors in the US market, which allows us to better capture granular sector-level shocks often washed out at the aggregate level. We find that the return spread between commodity futures with the lowest and highest loading of the CSC risk is 1.04% per month and significant at the 1% level. This result can be rationalized as shocks to the CSC risk precede impaired economic activities in the future. Overall, our paper sheds light on the pricing of commodity futures with a novel stock market crash risk factor.

This study explores the bidirectional forecasting between the realized volatility of VIX and S&P 500 index, especially the impact of asymmetric jumps and cojumps. Empirical results show that stock market jumps contain positive content for predicting the realized volatility of VIX while jumps contained in VIX can also improve predictive power for the realized volatility of the stock market. The positive and negative jumps of stock market and VIX have different asymmetric effects on realized volatility forecasts. Specifically, the negative jumps of stock index performs better whereas the positive jumps of VIX have stronger forecasting power, and each contains incremental information about the volatility prediction of the other party. Moreover, the cojumps enhance the forecasting ability, especially for the realized volatility prediction of VIX.

This paper investigates the role of different volatility components of the Shanghai International Energy Exchange (INE) oil futures, including intraday, overnight, and the first half-hour components, in forecasting Chinese stock market volatility. Using 5-min realized volatility (RV) as realized volatility measure (RM), the log-HAR models are applied to generate one-step-ahead forecasts for three Chinese stock indices (CSI 300, SHSE and SZSE). Our out-of-sample results show that the model extended with 5-min RV of INE oil futures does not generate more accurate volatility forecasts than the baseline log-HAR model. However, the overnight volatility of INE oil futures significantly improves forecasting accuracy. Our results are robust across different estimation schemes, estimation windows, out-of-sample periods, and evaluation methods. Additionally, using Bi-Power Variation (BPV) as an alternative RM yields consistent results. Overall, the results highlight the importance of incorporating the overnight volatility component of INE oil futures in forecasting Chinese stock market volatility.