互联网金融波动性的MCMC算法分析

Application of MCMC Algorithm in Internet Financial Volatility Analysis

基于金融市场的波动聚集、杠杆效应等种种特性,建立ARMA-GJR-GARCH模型并选取互联网金融板块日对数收益率的数据对互联网金融股市的波动性进行分析,采用MCMC方法对模型的参数进行贝叶斯估计以充分利用先验信息和样本信息使参数估计更精确并解决高维数值积分的不便.结果表明:互联网金融板块收益率存在比较明显的杠杆效应,利空消息比利好消息对互联网金融市场产生的冲击更大,且在互联网金融市场中,突发事所引起的风险可以被有效控制.

Based on the characteristics of volatility aggregation and leverage effect in financial market,ARMA-GJR-GARCH model was istablished witch selects the data of daily logarithmic yield of internet financial sector to analyze the volatility of internet financial stock market volatility.The MCMC method performs Bayesian estimation on the parameters of the model to make full use of the prior information and sample information to make the parameter estimation more accurate and solve the inconvenience of high-dimensional numerical integration.The research shows that there is a significant leverage effect on the profitability of the internet financial sector.The bad news has a greater impact on the internet financial market than the good news,and the risks caused by unexpected events in the internet financial market can be effectively controlled.