Realized volatility forecast of financial futures using time-varying HAR latent factor models

Abstract

We forecast realized volatilities by developing a time-varying heterogeneous autoregressive (HAR) latent factor model with dynamic model average (DMA) and dynamic model selection (DMS) approaches. The number of latent factors is determined using Chan and Grant's (2016) deviation information criteria. The predictors in our model include lagged daily, weekly, and monthly volatility variables, the corresponding volatility factors, and a speculation variable. In addition, the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models, including size, inclusion probabilities, and coefficients, are examined. We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts. Furthermore, the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.