Modeling Realized Volatility Dynamics with a Genetic Algorithm
The heterogeneous autoregressive model of realized volatility (HAR RV) is inspired by the heterogeneous market hypothesis and characterizes realized volatility dynamics through a linear function of lagged daily, weekly and monthly realized volatilities with a (1, 5, 22) lag structure. Considering that different markets can have different heterogeneous structures and a market's heterogeneous structure can vary over time, we build an adaptive heterogeneous autoregressive model of realized volatility (AHAR RV) whose lag structure is optimized with a genetic algorithm. Using nine common loss functions and the superior predictive ability test, we find that our AHAR RV model and its extensions provide significantly better out of sample volatility forecasts for the CSI 300 index than the corresponding HAR models. Furthermore, the AHAR RV model significantly outperforms all the other models under most loss functions. Besides, we confirm that Chinese stock markets' heterogeneous structure varies over time and the (1, 5, 22) lag structure is not the optimal choice. Copyright (c) 2016 John Wiley Sons, Ltd.