Effects of structural changes on the prediction of downside volatility in futures markets

We develop a heterogeneous autoregressive model of downside volatility (HAR-DV) model with structural changes (HAR-DV-SC) model to investigate the effects of structural changes on predicting downside volatility. Then we employ HAR-DV and HAR-DV-SC models to forecast downside volatilities in S&P 500 index, crude oil, gold, copper, and soybean futures markets. The in-sample analysis shows that structural changes contain in-sample information for predicting downside volatility. The out-of-sample analysis indicates that the HAR-DV-SC model outperforms the HAR-DV model, suggesting that structural changes contain incremental out-of-sample information on future downside volatility. These results are robust and have important implications for risk management of stakeholders.

Automated summary

This study develops a heterogeneous autoregressive model with structural changes to investigate the effects on predicting downside volatility. Results show that structural changes contain important information for predicting future downside volatility, and outperform the model without structural changes in out-of-sample forecasting.