基于随机波动性模型的中国股市波动性估计

Estimating volatility of Chinese stock market by stochastic volatility model

采用动态随机波动性模型实证研究了中国股票市场的波动性.通过基于马尔可夫链蒙特卡罗(MCMC)模拟的贝叶斯分析方法,较好地估计了随机波动性模型中的参数与波动性序列.基于中国股市数据进行的实证结果表明,与ARCH类模型相比,随机波动性模型能更好地描述股票市场回报的异方差和波动性的序列相关性.

The volatility of Chinese stock market is investigated using the dynamic version of stochastic volatility model, and Bayesian analysis based on MCMC is introduced to improve the parameters estimation in stochastic volatility model. Empirical results on Chinese stock market indicate that stochastic volatility model outperforms the ARCH model in capturing the heteroskedasticity and serial correlation of volatility of the stock market returns.