Journal of Futures Markets最新文献

This paper studies the volatility dynamics of futures contracts on crude oil, natural gas, and gasoline. An appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models is estimated using daily prices for our futures contracts between 2005 and 2023. Moreover, to assess the impacts of COVID-19 and the Russia–Ukraine conflict on volatility, we analyze these two subsamples. Overall, we find that: (i) the Bayes factor shows that the SV model with $��t�$-distributed innovations outperforms the competing models; (ii) crude oil contracts with different expiry dates may require the introduction of leverage effects; (iii) the $��t�$-distributed innovations remain the appropriate model for the COVID-19 subsample, while jumps are needed in the conflict period; and (iv) other Bayesian criteria more appropriate to short-term predictive ability—such as the conditional and the observed-date deviance information criterion—suggest other rank order to model our futures contracts, despite the agreements for the best models.

We provide new evidence on the response of investment to uncertainty, using granular and high-frequency (weekly) data on domestic oil drilling and oil prices since 2012, corresponding to the period of widespread horizontal drilling and hydraulic fracturing in the United States. Weekly data permits much weaker identifying restrictions than is required with monthly data that is common in the literature. We measure domestic drilling activity by the number of rigs drilling for oil, and we measure oil uncertainty by implied volatility from options on oil futures and the return on delta-neutral straddles from options on oil futures. We show that the number of oil drilling rigs are tightly linked to both oil prices and oil uncertainty, and we find that oil uncertainty significantly decreases the number of drilling rigs, with a one standard deviation increase in uncertainty reducing the number of drilling rigs by up to 5%.

We use prevailing machine learning models to investigate the predictability of futures returns in 22 commodities with commodity-specific and macroeconomic factors as predictors. Out-of-sample prediction errors for the majority of futures contracts are lowered compared with those obtained by the baseline models of AR(1) and forecast combinations. Using Shapley values to explain feature importance, we identify dominant predictors for each commodity. A long–short portfolio strategy based on monthly light gradient-boosting machine predictions outperforms the benchmark linear models in terms of annual return, Sharpe ratio, and max drawdown.

Motivated by the puzzling null impact of high-frequency-based jumps on future volatility, this paper exploits the rich information content in high-frequency jump intensity with a mark structure under the heterogeneous autoregressive framework. Our proposed model shows that harnessing jump intensity information from the marked Hawkes process leads to significantly superior in-sample fit and out-of-sample forecasting accuracy. In addition to statistical significance evidence, we also illustrate the economic significance in terms of trading efficiency. Our findings hold for a variety of competing models and under different market conditions, underlying the robustness of our results.

This study evaluates the dual role of hedging pressure (HP) in oil futures markets and analyses its effects on weekly oil volatility. We find that HP driven by hedgers' insurance demands is negatively related to volatility, while HP driven by speculators' short-term liquidity demands is positively related to volatility. Oil volatility tends to be more responsive to speculators' short-term liquidity demands than variations induced by hedgers' insurance demands. These channels are also significant determinants of volatility in inverted and normal markets, with the effects being more pronounced in inverted markets. Under low financial and business-cycle risk environments, the two HP channels typically have a measurable impact on volatility. These opposing effects of HP on weekly volatility provide empirical support on the significance of the dual role of hedgers in oil markets, as price insurance seekers and as short-term liquidity providers.

Leveraging tick-by-tick data, this study provides the first analysis of Shanghai International Energy Exchange (INE) bonded copper futures' performance in market quality and price discovery. In particular, we investigate the effects of the market opening (listing of INE bonded copper futures) on Shanghai Futures Exchange (SHFE) copper futures' market quality and price discovery. Our results show that the market quality and price discovery of INE bonded copper futures in the first year of the listing is not promising. Our synthetic control method results suggest that market openness does not significantly reduce SHFE copper futures' market quality in terms of activity, liquidity, and volatility. Moreover, market openness does not significantly reduce the SHFE copper futures' price discovery effectiveness. Overall, the performance of new INE bonded copper futures needs improvement, while its listing did not disrupt SHFE copper futures. Our results suggest that a dual-contract mode is an alternative option for internationalization in China's commodity futures markets.

This study investigates the potential effects of environmental factors on fluctuations in agricultural commodity futures markets, by constructing a new category of daily exogenous predictors related to air pollution, weather, climate change, and investor attention. The empirical results from out-of-sample analyses suggest that the heterogeneous autoregressive (HAR) model incorporating all these exogenous predictors is more likely to outperform other HAR-type models. Additionally, economic evaluations demonstrate the superior performance of models incorporating investors' attention to climate change or extreme weather as predictors. While not all exogenous predictors are equally important for volatility forecasts, adopting appropriate variable selection methods to handle different sets of exogenous predictors can lead to better performance than the HAR benchmark. With the inclusion of air pollution or weather factors in the HAR model, a portfolio with an annualized average excess return of 16.2068% or a Sharpe ratio of 10.0431 can be achieved for the wheat futures, respectively.

This study presents a firm-specific methodology for extracting implied default intensities and recovery rates jointly from unit recovery claim prices—backed by out-of-the-money put options—and credit default swap premiums, therefore providing time-varying and market-consistent views of credit risk at the individual level. We apply the procedure to about 400 firms spanning different sectors of the US economy between 2003 and 2019. The main determinants of default intensities and recovery rates are analyzed with statistical and machine learning methods linking default risk and credit losses to market, sector, and individual variables. Consistent with the literature, we find that individual volatility, leverage, and corporate bond market determinants are key factors explaining the implied default intensities and recovery rates. Then, we apply the framework in the context of credit risk management in applications, like, market-consistent credit value-at-risk calculation and stress testing.