基于多分辨率分析和极值理论的集合VaR模型

摘要

In order to capture time-varying features of volatility of asset price,multi-resolution analysis (MRA) was used to decompose financial returns into orthogonal components in different time domains.For each component,a certain ARMA-GARCH model was built.Extreme value theory (EVT) was then introduced so as to model the fat-tail of financial returns,and an MRA-EVT model was constructed.Finally,the proposed model was applied to predict VaR of CSI 300 index,and compared with traditional models,such as ARMA-GARCH model,unconditional EVT model and MRA model.Empirical results show that the MRA-EVT model significantly improves the accuracy of VaR estimation.%为了捕捉金融资产价格波动的多尺度时变特征,利用多分辨率分析(multi-resolution analysis,MRA)将收益率序列分解成不同时域上的正交分量,并对各分量序列分别建立适当的ARMA-GARCH模型,在此基础上引入极值理论(extreme value theory,EVT)对收益率的厚尾性进行建模,构建了一种MRA-EVT模型.将该模型应用于沪深300指数的VaR预测.实证研究结果表明,与传统ARMA-GARCH模型、无条件EVT模型和MRA模型相比,该MRA-EVT模型显著提高了VaR的预测绩效.