Journal of Futures Markets最新文献

Agricultural producers face significant price risk, yet studies consistently find farmers use futures and options much less than predicted by optimal hedging models. Using nationally representative farm-level data, we investigate two underexplored factors that can affect the use of exchange-traded derivatives: historical basis risk and cash constraints. We show that corn and soybean farms located in counties that experienced a large, negative change in the corn basis between planting and harvest (a negative basis shock) in the last 5 years are less likely to use futures contracts (6–12 percentage points) and options contracts (3–18 percentage points), but have a greater likelihood of marketing contract use (14–24 percentage points). We also find that farms with greater cash constraints (lower cash holdings) are less likely to use futures and options. We show that crop insurance, storage, and cooperative membership are complementary when using futures and options.

This study evaluates the market downturn connectedness between S&P 500 index funds and real-time markets (gold and WTI) during the COVID-19 pandemic and the Russia-Ukraine wars. Using the TVP-VAR approach, we explored the significant connectedness among these markets during both crisis episodes. The S&P 500 Index Fund (State Street S&P 500 Index Fund Class N) is the net risk spillover receiver in the system, whereas S&P 500 Index funds (all others) are significant volatility spillover transmitters during the COVID-19 and Russia-Ukraine wars. Furthermore, gold and WTI receive net risk spillovers in both crises. However, all S&P 500 index funds are also pairwise and extensively connected with real-time markets (gold and WTI) in the COVID-19 and Russia-Ukraine wars. This study offers potential investment insights for shareholders, traders, speculators, and portfolio managers in these markets.

We extend the literature on empirical asset pricing to the Chinese options market by building and analyzing a comprehensive set of return prediction factors using various machine learning methods. In contrast to previous studies for the US market, we emphasize the uniqueness of this emerging market, investigate daily hedging strategies to construct delta-neutral portfolios, and identify the most important characteristics for return prediction. Short-selling restrictions in China's financial market diminish the effectiveness of spot hedging, whereas delta-neutral portfolios based on futures hedging deliver substantial improvements in both annual returns and Sharpe ratios. Machine learning models not only outperform the IPCA benchmark, but also demonstrate strong generalization ability when applied to newly issued option contracts. The out-of-sample performance remains economically significant after accounting for transaction costs.

Building on prior literature that has demonstrated the effectiveness of various uncertainty-related indicators in enhancing the accuracy of crude oil volatility forecasting, this paper first investigates the type and persistence of the impact of changes in these indicators on volatility and then compares these indicators across different scenarios to determine the optimal strategy for their implementation. We employ a more generalized approach by utilizing the GARCH–MIDAS–AES model, which accommodates features that vary with different indicators. The empirical results, based on data from 1997 to 2022, underscore the importance of considering threshold and leverage effects. We also identify two types of impact: directional and nondirectional. Furthermore, among the uncertainty indicators examined, our findings affirm the predictive prowess of the Financial Uncertainty indicator in the majority of cases. However, during periods of global crisis, the Index of Global Real Economic Activity emerges as a more practical choice.

This paper introduces an imitation learning deep hedging (ILDH) algorithm, which bridges the Black-Scholes-Merton (BSM) model with deep reinforcement learning (DRL) to address the option hedging problem in incomplete real markets. By leveraging imitation learning, the DRL agent optimizes its hedging policy using both freely explored action samples based on real trading data and corresponding action demonstrations derived from the BSM model. These demonstrations serve as data augmentation, enabling the agent to develop a meaningful policy even with a relatively small training data set and enhancing the management of tail risk. Empirical results show that ILDH achieves higher profit, lower risk, and lower cost in the Chinese stock index options market, as compared with other deep hedging algorithms and traditional delta hedging method. This outperformance is robust across call and put options, different transaction cost conditions, and varying levels of risk aversion.

This paper introduces an innovative two-step approach for identifying implied volatility (IV) mispricing across a large cross-section, moving beyond the traditional volatility forecasting framework. The two-step process disentangles the contributions of historical volatility and other firm-specific characteristics, isolating the residual as the IV mispricing. Different from traditional IV misvaluation proxies, which primarily focus on 1-month at-the-money (ATM) options, our method demonstrates broader applicability. It accommodates options with wider maturities and extends to both ATM and out-of-the-money (OTM) call and put options. Applying a long-short delta-hedged options trading strategy, using the IV mispricing, achieves a high information ratio (IR). When incorporating short- and long-term historical volatility trends as conditions, while returns remain relatively unchanged, portfolio volatility is significantly reduced, further enhancing the IR to 4.093. This approach provides a robust predictive signal for option returns and remains resilient to transaction costs, consistently outperforming alternative signals, as validated through double-sorting analysis.