The Characteristic Of The Realized Volatility In Chinese Stock Market Research Based On The HMH

Volatility and its dynamics are central to asset and derivative pricing, effectiveportfolio choice and risk management, which has been a hot topic of finance area.Recently, with the rapid development of the computer and communicationtechnology, the cost of data recording and storing has been significantly reduced,which makes the high frequency data more availably for the research of the financialasset price volatility and the market microstructure. Andersen and Bollerslev(1998)kick-started a flurry of research on the use of high-frequency of intraday data formeasuring and forecasting daily volatility-“realized volatility(RV)”, compared to thedaily returns of the financial assets, the RV may measure the volatility moreprecisely. According to the heterogeneous market hypothesis proposed by Müller(1993), the Heterogeneous Auto-Regressive Realized Volatility model(HAR-RVmodel) has been proposed by Corsi(2004), utilized volatility components of differenttime resolutions derived from representing some different trade strategy of theparticular traders make their relevant yet marginal contribution on the wholevolatility to capture the empirical memory persistence of volatility in astraightforward and parsimonious manner. While its structure implies that it neverbelongs to a genuine long memory model, it possesses a well-defined economyimplication and can be estimated in a much simpler way-OLS.Leverage effect and volume-volatility relations are the main characteristics ofthe stock market fluctuation. In this paper, we propose an extended version of theHAR model which considers both asymmetric responses of the realized volatility toprevious negative returns and the rice-volume relationship at all the consideredfrequencies, which has not been done by the extant researcher as far as the author’sknowledge, and we called it Leverage Heterogeneous Auto-Regressive RealizedVolatility with Volume model (LHAR-RV-V model). The proposed model is appliedto empirical analysis with the 1 minute high frequency data of Shenzhen 300 index.The empirical results show that the model can well capture the long memory and theleverage effect characteristics, and the leverage effect has certain sustainability. Inaddition, the past different cycle volume joined in the model can not only reflect therelationship between the volume and price in a more sophisticated way, but alsoimprove the predictive power of the model partly.