The Empirical Research On The Volatility Of Chinese Stock Market

The volatility is not only a universal phenomenon existing in the financial time series, but also a core research question to describing the financial market. There are two types of models used in researching financial volatility-autoregressive conditional heteroskedasitic (ARCH) model and stochastic volatility (SV) model. These two types of models have different characters and lots of expanding models, such as GARCH-type models,heavy-tail SV models and so on. But the quality of these models for simulating the financial time series does not have a conclusion. Therefore we need to introduce a series of indices to evaluate the accuracy of the forecasting ability of models.This article introduced ARCH-type models, SV-type models and the method to estimate the parameters in these models. First, the GARCH model, the TGARCH model, the EGARCH model and the GARCH-M model in the ARCH model system were analyzed, which is based on the data of returns ratio series of Shanghai stock market and Shenzhen stock market. Then, the SV-N model, the SV-T model, the SV-GED model in the SV model system were analyzed according to the Bayesian theorem. A Markov chain Monte Carlo algorithm procedure with Gibbs sampler was designed to estimate the models' parameter through the WinBUGS software. After the comparative analysis between Shanghai and Shenzhen stock market, we discovery that Shanghai Stock market and Shenzhen Stock market all display the strong cluster effect, leverage effect and leptokurtic effect. Shanghai Stock market has the stronger leptokurtic effect compared to Shenzhen Stock market. But the Shenzhen Stock market's volatility level is higher than Shanghai Stock market, the risk is also higher.The out-of-sample forecasting ability of ARCH models system were compared with SV models system using the RMSE, MAE, MAPE, TIC, LL, LE indices under the Shanghai and Shenzhen Stock market. The analysis discovered that the forecasting ability of SV models is superior than ARCH models. And SV models can profoundly descript the leptokurtic effect in our country Stock market better. In the SV models, the forecasting accuracy of Heavy-ail SV model is superior than the SV-N model in Shenzhen Stock market. At most times, the best model to forecast the volatility of Stock market is the SV-T model. Therefore, we may draw the conclusion, stochastic volatility model is more fit for descript our country Stock market's volatility characteristics.