缺失数据情形下期望收益率和波动率估计的潜变量MCMC抽样方法

Latent Variable MCMC Sampling Method for Estimates of Yield and Volatility in Case of Missing Data

在缺失数据情形下,采用贝叶斯方法研究了Black-Schole模型的参数估计问题.首先,对Black-Schole模型下的风险资产序列进行分析并建立了贝叶斯模型.其次,采用潜变量Gibbs抽样方法获取期望收益率和波动率的估计值.最后,以2018年8月3日的沪深300指数为仿真对象,在无信息先验下进行了实证分析.

In this paper,the Bayesian method is used to study the parameter estimation problem of Black-Schole model in case of missing data.Firstly,the Bayesian model is established to analyze the risk asset sequence under the Black-Schole model.Secondly,the latent variable Gibbs sampling method is used to obtain the estimates of yield and volatility.Finally,the HS 300 Index on August 3,2018 is used as the simulation object,and the Bayesian estimates of yield and volatility is analyzed with non-informative prior.