Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging

Heterogeneity, volatility persistence, leverage effect and fat right tails are the most documented stylized features of realized volatility (RV), which introduce substantial difficulties in econometric modeling that requires some rigid distributional assumptions. To accommodate these features without making these assumptions, we study the quantile forecasting of RV by proposing five novel dynamic model averaging strategies designed to combine individual quantile models, termed as dynamic quantile model averaging (DQMA). The empirical results of analyzing high-frequency price data of the S&P 500 index clearly indicate that the stylized facts of RV can be captured by different quantiles, with stronger effects at high-level quantiles. Therefore, DQMA can not only reduce the risk of model uncertainty but also generate more accurate and robust out-of-sample quantile forecasts than those of individual heterogeneous autoregressive quantile models.