基于EVT-POT-SV-MT模型的极值风险度量  

Extreme Risk Measurement Based on EVT-POT-SV-MT Model

作　　者：董耀武[1] 周孝华[1] 姜婷[1] 

机构地区：[1]重庆大学经济与工商管理学院,重庆400030

出　　处：《管理工程学报》2012年第1期119-124,共6页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(70473107)

摘　　要：针对金融资产收益的异常变化,采用SV-MT模型对风险资产的预期收益做风险补偿并捕捉收益序列的厚尾性、波动的异方差性等特征,将收益序列转化为标准残差序列,通过SV-MT模型与极值理论相结合拟合标准残差的尾部分布,建立了一种新的金融风险度量模型——基于EVT-POT-SV-MT的动态VaR模型。通过该模型对上证综指做实证分析,结果表明该模型能够合理有效地度量上证综指收益的风险。VaR （Value at Risk） is an important method of risk management, which is used to measure the maximum possible loss of a portfolio in a selected confidence level at a given period of time in the future. The primary purpose of this method is to describe the volatility of financial time series as accurate as possible. Analysis methods are primarily used to predict management risk in VaR. The accuracy of the analysis result is mainly reflected in the financial asset of the error term distribution settings and volatility forecast. Because of the variability of financial markets, the traditional non-conditional normal distribution assumption is no longer applicable, and the conditional normal distribution or conditional distribution which is more characterized by fat tail is more in line with actual market volatility and return distribution.

关 键 词：SV-MT 风险补偿 MONTE CARLO模拟 极值理论 PARETO分布 

分 类 号：F830[经济管理—金融学]