Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility

We forecast the realized volatilities of China's agricultural commodity futures (corn, cotton, palm, wheat, and soybean) using a set of multivariate heterogeneous autoregressive (MHAR) models. We consider different error structures to capture the co‐movement of volatility (co‐volatility) between commodity futures to obtain out‐of‐sample forecasts of the realized volatilities of agricultural commodity futures at daily, weekly, and monthly forecast horizons. We also consider global oil volatility as an additional exogenous predictor and assess forecast precision based on both statistical and economic value measures. The results show that the MHAR model with a co‐volatility error structure is superior in predicting the volatility of commodity futures. The most accurate predictions are of corn, cotton, and wheat futures volatility. Interestingly, for all the commodities, the forecasting accuracy noticeably improves as the forecast horizon increases. However, from an investment perspective, the highest economic value is gained from medium‐horizon forecasts, and the economic value of forecasts is highest for less risk‐averse investors.