Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect

We investigate the properties of the realized volatility in Chinese stock markets by employing the high-frequency data of Shanghai Stock Exchange Composite Index and four individual stocks from Shanghai Stock Exchange and Shenzhen Stock Exchange, and find that the volatility exhibits the properties of long-term memory, structural breaks, asymmetry, and day-of-the-week effect. In addition, the structural breaks only partially explain the long memory. To capture these properties simultaneously, we derive an adaptive asymmetry heterogeneous autoregressive model with day-of-the-week effect and fractionally integrated generalized autoregressive conditional heteroskedasticity errors (HAR-D-FIGARCH) and use it to conduct a forecast of realized volatility. Compared with other heterogeneous autoregressive realized volatility models, the proposed model improves the in-sample fit significantly. The proposed model is the best model for the day-ahead realized volatility forecasts among the six models based on various loss functions by utilizing the superior predictive ability test.