Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches

In order to reduce the uncertainty associated with a single predictor model, we incorporate the bagging and combination approaches into a HAR model with the lags of realized volatility and other potential predictors to forecast the realized volatility of agricultural commodity futures in China. We evaluate the performances of the two approaches by employing the mean square forecast error (MSFE) loss function, the modified DM test and the model confidence set (MCS) test at the multiple horizons over the three out-of-sample periods. We find that the realized forecasts from the HAR model with bagging and principal component (PC) combination approaches produce the lowest MSFE at relatively longer forecast horizons. We also find that the simple average of the forecasts from the HAR models with bagging and PC combination methods leads to a further reduction in MSFE, suggesting that they are the effective methods to forecast the realized volatility of agricultural commodity futures in China.