Asymmetric Information and Volatility Forecasting in Commodity Futures Markets

• THSV models can better describe return and variance dynamics of futures.
• Alternative THSV models outperform the standard THSV model in fitting data.
• Positive and negative shocks have asymmetric effects on return and variance.
• It is more important to model asymmetries in mean than those in variance.
• Modeling asymmetries in return and variance helps improve VaR forecasts.

This paper investigates the asymmetric characteristics of returns and volatilities of various Chinese commodity futures within the threshold stochastic volatility (THSV) framework with various distribution assumptions. To gauge the capabilities of THSV models in volatility forecasting, the values-at-risk (VaRs) for both long and short positions in these futures are estimated and analyzed. We demonstrate that the asymmetric THSV model outperforms the corresponding symmetric SV model, and that the THSV models with non-normal distributions can better fit the futures data than the standard THSV model. Our results clearly indicate that positive and negative news have asymmetric effects on the mean, variance, and variance persistence of all futures under consideration. We also document that modeling both the mean and variance asymmetries and the fat-tailed feature in return distributions is particularly important to accurately forecast the VaRs for long and short trading positions in commodity futures.