The Contrast of Parametric and Nonparametric Volatility Measurement Based on Chinese Stock Market

Abstract

Most procedures for modeling and forecasting financial asset return volatilities rely on restrictive and complicated parametric GARCH or stochastic volatility models. The method of realized volatility constructed from high-frequency intraday returns is an alternative choice for volatility measurement. In this paper we make an empirical analysis on Chinese stock index data by using the method of nonparametric realized volatility. We find that the realized volatility can describe the Chinese stock index volatility very well. The original Chinese stock index return series show obvious leptokurtic, fat-tailed relative to the Gaussian distribution.We show that the return series standardized instead by the realized volatility are very nearly Gaussian distribution, and we find that the four minutes is a better choice as the best time interval to describe the volatility of Chinese stock market. We also make a contrast with the popular method of GARCH model, but the return series standardized instead by GARCH model don’t accord with Gaussian distribution. The result shows that the realized volatility can describe the dynamic behaviors of Chinese stock market well. In a way, it indicates that the Chinese stock market is effective.