基于马尔科夫随机波动和极值理论的风险测度  

Risk Measurement Based on Markov Stochastic Volatility and EVT

作　　者：姬新龙[1] 周孝华[1] 

机构地区：[1]重庆大学经济与工商管理学院,重庆400030

出　　处：《中国管理科学》2014年第10期44-51,共8页Chinese Journal of Management Science

基　　金：国家自然科学基金资助项目(70473107)

摘　　要：针对金融资产波动的时变、聚集以及状态转换等特征,将马尔可夫转换模型和随机波动模型相结合,同时考虑波动尾部的状态分布,构建MSSV-t模型,然后将收益序列转化为标准残差序列,在此基础上,应用EVT模型对标准残差进行建模,进而构建基于MSSV-t-EVT的VaR测度模型,最后对该模型的有效性进行检验。研究发现:MSSV-t-EVT模型能够有效识别上证指数(SSCI)的波动转换特征,并且能合理地测度该指数的收益风险,尤其在高的置信水平下表现更好。研究结论表明MSSV-t-EVT模型能较为准确的刻画股市剧烈波动的事实,可用于交易风险控制和对市场异常波动的预警。In order to capture the characteristics of changed, gathered and state transitions of the fluctuations in financial assets in the stock returns data, Markov chain is introduced into the SV model to build the MSSV-t model, then the extreme value theory（EVT） is combined to measure the VaR. Use the Shanghai Composite Index for the empirical analysis. Finally the effect of MSSV-t-EVT model is analyzed with Backtesting, the results shows that the MSSV-t-EVT model can portray the fluctuation characteris- tics of financial yield effectively, especially the extreme fluctuation characteristics. Backtesting results shows, the application of MSSV-t-EVT model to measure the risk of comprehensive Index is reasonable and effective. In particular the higher the confidence level, the higher the accuracy, These results indicate that MSSV-t-EVT model has a better description and warning functions than the traditional linear risk measurement model. It can be used for the risk control of investment portfolio, and also for the warning of abnormal fluctuations by financial regulators.

关 键 词：MSSV-t模型 极值理论 波动状态转换 风险测度 

分 类 号：F831.5[经济管理—金融学]