Journal of Futures Markets最新文献

The development of deep learning technique has granted firms with new opportunities to substantially improve their risk management strategies for sustainable growth. This paper introduces a novel deep learning-based financial hedging (DL-HE) strategy to leverage the salient ability of deep learning in extracting nonlinear features from complex high dimensional data, thus boosting the management of inventory risks arising from erratic commodity prices. Using real-world data, we find that the average annualized economic benefit of the proposed strategy is at least 1.21 million CNY for a typical aluminum firm carrying an average level of inventory in China, as compared with those of the traditional hedging strategies. Further analysis reveals that such an economic benefit can largely be explained by the efficacy of the proposed DL-HE strategy in terms of significantly improving return while still effectively controlling risk. Moreover, the superior of this strategy remains robust when extending to copper and zinc.

For stock and index options in the United States, OptionMetrics records prices at 3:59 p.m., not 4:00 p.m. as assumed in previous literature. The resulting 1-min time discrepancy with closing share prices creates artificial variability in implied volatility spreads and strongly affects market-wide spreads. It leads to particularly large distortions at the onset of the COVID-19 pandemic. For index options in Europe, OptionMetrics data show large deviations from put-call parity even though the original option prices match the parity exactly. Finally, the implied volatilities of stock options in Europe show clusters of exceptional deviations due to incorrect dividend information.

We explore the effects of financial regulatory arbitrage on commodity pricing. We examine two types of financial arbitrage: capital-control arbitrage, in which commodities are imported to circumvent capital controls and profit from disparities in interest rates between domestic and international markets, and dual-track interest-rate arbitrage, in which commodities are utilized as collateral to capitalize on domestic dual-track interest-rate spreads. Our findings demonstrate that both forms of arbitrage positively affect commodity price returns. However, they affect the inverse relationship between inventory and convenience yield differently. While capital-control arbitrage can either amplify or weaken this relationship, dual-track arbitrage makes it less negative.

We exploit a 2018 exchange-mandated increase of the maximum order size in some—but, crucially, not all—US agricultural futures markets, to link exogenous constraints on order placement and execution, price volatility, and market liquidity. The old maximum size of 2500 contracts was binding: demand exists for placing and executing much larger orders. Limit-order book depth at the best bid and ask increases dramatically after the exchange quadruples the maximum order size. Amid relatively stable volatility, bid-ask spreads narrow, and the price impact of large trades falls. In sum, we find that market quality can improve after an increase in maximum order and trade size.

This study employs minimum spanning tree and generalized forecast error variance decomposition methods to investigate the connectedness and risk spillovers across China's commodity sectors from January 2016 to December 2021. The results show that total connectedness within the commodity system is time varying. Chemical is the main risk driver, while other sectors occasionally dominate the system. These two methods achieve consistent results in identifying the systemically important sector and dynamic connectedness. In addition, we find that Chinese economic policy uncertainty and the investor sentiment index have significant impacts on total connectedness. Our findings have implications for preventing systemic risk for policymakers and managing commodity portfolio risk for investors.

The seasonal risk of wheat, corn, and soybean is modeled by a novel seasonality filter based on a generalized ridge regression. Then, using a component GARCH model, seasonal risk is combined with event risk and a short-term risk dynamics. The resulting model is robust, generates seasonal patterns related to the crop cycle, and significantly outperforms the standard GARCH(1,1) in terms of out-of-sample risk prediction. Results are relevant for risk management and portfolio construction.

This study addresses key issues of market efficiency in weak global futures markets, focusing on the intricate relationship between market sentiment and options pricing. Employing rolling variance ratio tests and information-sharing models for market dynamics analysis, and supplemented with Granger causality tests and impulse response findings, it reveals a significant, unidirectional impact of market sentiment on options pricing, especially during periods of heightened sentiment. These insights underscore the importance of considering time dynamics in market behavior analysis, offering a novel perspective on futures and options market understanding.

We find that early exercise premiums of exchange-traded single-stock American puts, in excess of the GBM-world premium, can negatively predict future stock returns. Simulations suggest that asset-value jumps, especially the mean jump-size, can positively drive this excess premium, while jump-size can also negatively induce the implied volatility (IV) spread of equivalent American option-pairs. Empirically, controlling for the effect of jump-size in excess premiums, the premium loses its predictive power. Furthermore, controlling for the excess premium or jump-size, IV spreads' predictability shown in the literature also diminishes. Our evidence survives under alternative explanations like informed trading, stock mispricing or market frictions.