Journal of Futures Markets最新文献

This study develops a novel approach for improving stock return volatility forecasts using volatility index information with the entropic tilting technique. Unlike traditional linear heteroskedasticity autoregressive methods with option-implied information, we first derive predictive densities from traditional models, and then tilt using both the first and second moments of the risk-neutral distribution, which enables us to capture the nonlinear effect in our specification. The empirical findings demonstrate a substantial enhancement in the forecasting accuracy of all models once the first- and second-moment information is considered, where the improvement is both statistically and economically significant. These results have important implications for risk management in well-established derivatives markets.

Climate-change futures provide a platform for low-carbon portfolios and energy market risk hedging. Climate changes induce uncertainty in energy-commodity markets. We investigate the potential of diversifying and hedging energy-commodity market risk with climate-change futures, using dynamic conditional correlation (DCC)-ordinary least squares (OLS) incorporating quantile-dummies and cross-quantilogram (CQ) approaches. DCC-OLS models reveal that the World and USA climate-change futures exhibit that they can be diversifiers for oil, ethanol, gasoil, and gasoline. These futures also exhibit hedging features for natural gas, coal, and heating oil. Euro climate-change futures demonstrate hedging capabilities for all energy commodities except oil and gasoil. World, USA, and Euro climate-change futures have the potential to serve as safe-haven financial instruments in the face of the high volatility of Brent crude oil, gasoil, and heating oil. The CQ reveals that World, USA, and Euro climate-change futures exhibit hedging and safe-haven capacity against oil, natural gas, coal, gasoil, gasoline, and heating futures. Climate-change futures may protect financial investments during extreme volatility in energy commodities.

To address the ongoing contention surrounding the impact of financialization, this study adopts a ripple-spreading network model to analyze the transmission of information across 13 globally significant commodity markets. By juxtaposing the pre- and postfinancialization periods, notable disparities in spillover magnitude are discerned, with overall effects registering at 58% and 85%, respectively. Moreover, the postfinancialization period exhibits accelerated spillover dynamics, necessitating a reduced timeframe (less than 1000 units) in contrast to the prefinancialization period (approximately 2000 units). Furthermore, a heightened interconnectedness among energy, metal, and agricultural futures is evident in the postfinancialization period. These findings furnish compelling evidence regarding the ramifications of financialization on commodity markets.

Delta-hedged option selling strategies typically yield positive returns, owing to the volatility risk premium embedded in the option price. Recent research based on S&P 500 options has found a day–night asymmetry in option returns. We find a similar disparity in the returns for short Nifty option strategies. Positive and significant overnight option returns are accompanied by negative intraday returns. The day–night asymmetry is robust across option categories and subsamples but weaker on days with significant jumps in the underlying. We confirm that the variance risk premium earned by option sellers is mainly a reward for overnight risk.

Based on the formation and evolution of systemic risk, we study the high-low volatility spillovers between economic policy uncertainty (EPU) and commodity futures and identify the source of risk accumulation and risk outbreak, as well as the corresponding contagion mechanisms. Upon comparing topological characteristics on each volatility layer, our results demonstrate that high and low volatility spillover networks have different network characteristics and evolution behaviors. At the system level, high volatility spillovers are relatively stronger than spillovers in in low volatility network, while the risk propagation efficiency in the low volatility network is higher. At the market level, EPU is not only an important risk-emitter but also a risk-recipient most of the time. Additionally, compared with high volatility network, low volatility network characteristics have greater predictive ability for risk spillover among commodity futures, which means that it contains additional information and provides early warning signals for financial stress.

This study analyzes the risk-adjusted performance of commodity exchange-traded funds (ETFs) across diverse market conditions. Examining monthly returns from December 2004 to June 2022, our findings suggest that commodity ETFs underperformed US stocks, indicating limited diversification benefits. However, we document positive α during turbulent market periods like the COVID-19 crisis, signifying potential resilience. Furthermore, our factor regressions reveal that shifts in the global commodity price index and disposable personal income significantly influence commodity ETFs' excess returns, pointing to broader economic and income-related trends. Commodity ETFs exhibit lower Value-at-Risk and Expected Shortfall compared to stock market indices, indicating reduced downside risk exposure for investors. Given the increasing popularity of commodity ETFs, these insights hold substantial significance for market participants.

We examine a comprehensive set of systematic and firm-specific determinants of the credit default swap (CDS), using a two-step approach to explore the factor's effect on CDS spread changes. We show that systematic factors are important and account for the most changes in the CDS spreads (with average $��\; ��R^2��$ of 35%), while firm-specific factors are limited (with $��\; ��R^2��$ of 5% in panel regression) with only 4 out of 28 firm-specific factors being significant. It implies that the systematic factors are overlooked in the literature, and they can provide many implications for practitioners in CDS pricing and the firm's credit risk management.

This paper develops analytical approximations for volatility index (VIX) option pricing under nonaffine generalized autoregressive conditional heteroskedasticity (GARCH) models as advocated by Christoffersen et al. We obtain the approximation formulae for pricing VIX options and then evaluate their performance with three expansions under four empirically well-tested models. Our numerical experiments find that the weighted $��\; ��{ℒ}^2��$ expansion generated by the fat-tailed weighting kernel can significantly reduce approximation error over the Gram-Charlier expansion; the Taylor expansion of conditional moments can lead to divergence for parameters with certain high persistence in the affine GARCH, nonlinear asymmetric GARCH, and Glosten-Jagannathan-Runkle GARCH models, while surviving during high persistence in the exponential GARCH.

This study offers a nuanced examination of the interplay between climate policy uncertainty (CPU) and three categories of the US commodity futures returns (metals, energy, and soft commodities). By integrating a regression framework with a Markov regime-switching approach, our results uncover both linear and nonlinear effects of CPU in varying volatility contexts. This comprehensive methodological approach sheds light on CPU's diverse impacts across various types of commodity futures. The findings illustrate CPU's notable influence on metal and energy futures in low-volatility regime, contrasted with its more pronounced effect on soft commodities during high-volatility regime. These insights are pivotal for investors strategizing in light of CPU, and underscore the significance of renewable energy in alleviating climate policy uncertainties within commodity markets.