经济数学2017，Vol.34Issue(1)：1-5,5.
厚尾随机波动率模型的贝叶斯参数估计及实证研究
Bayesian Estimation of the Thick-Tailed Stochastic Volatility ModelEmpirical Study of Shanghai Composite Index
摘要
Abstract
To solve the problem that the current stochastic volatility model cannot describe the characteristics of parameters' time-changing property,this paper extended the parameter estimation methodology of the thick-tailed stochastic volatility model, and chose the Shanghai Composite Index from May.2013 to June.2016 as empirical study samples which fluctuated several times.Furthermore, this paper established the MCMC procedure based on Gibbs sampling method to simulate the model.The result indicates that taking chi-square distribution as the prior distribution of the thick-tailed parameter can describe thick-tailed property of the data precisely and can get more accurate parameter estimation result.According to the reasons above, this paper argues that SVT model can characterize the Chinese stock market's volatility and long-term memory properties efficiently.关键词
SV模型/贝叶斯估计/MCMC方法Key words
stochastic volatility model/Bayesian estimation/MCMC method分类
数理科学引用本文复制引用
黄文礼,张睿轩..厚尾随机波动率模型的贝叶斯参数估计及实证研究[J].经济数学,2017,34(1):1-5,5.基金项目
国家社科青年基金(12CJY022)资助 （12CJY022）
浙江省自然科学基金(LY14G030013)资助 （LY14G030013）
