Improve Volatility Forecasting with Realized Semivariance-Evidences from Intra-Day Large Data Sets in Chinese

Realized semivariance is reported more informative than realized variance. This paper employs a new modeling approach for the realized semivariance inspired by Chou (2005) in order to capture the asymmetry of volatility in financial markets better. With high frequency data from Shanghai stock market in Chinese, the empirical results, which uses four types of volatility proxies including squared daily returns, daily high-low price ranges, realized variance, and realized range consistently, indicate that this model sharpens the forecast power of existing volatility models in terms of GARCH type models. Mincer-Zarnowitz regression and four loss functions are employed for the assessments in out of the sample forecasting.