基于时变高阶矩波动模型的VaR与ES度量被引量：24

Calculating VaR and ES based on volatility models with time-varying higher-moments

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摘要金融波动性建模经历了从常数高阶矩到时变高阶矩的发展历程.文章扩展了现有的针对时变高阶矩波动模型风险测度效果的研究:首先,以沪深300指数和其它世界股市若干重要指数为例,通过采用"从简单模型到复杂模型"的估计步骤,实现对时变高阶矩波动模型的估计,进而运用Gram-Charlier扩展分布获得对VaR(value-at-risk)和ES(excepted shortfall)两种不同风险测度的计算值;然后,分别利用非条件覆盖检验(unconditional coverage test)和基于自举法(Bootstrap)的后验分析方法,实证对比了时变高阶矩和常数高阶矩两类模型的适用范围和精确程度.研究结果表明:就所考察的若干指数样本而言,时变高阶矩模型不仅能够较好地刻画金融价格波动的整体动力学特征,并且总体来讲,在市场风险测度准确性方面也要优于常数高阶矩波动模型. This paper extends prior studies on risk estimation of volatility models with time-varying higher-mo- ments. With several important stock market indices, we adopt a ＂from simple model to complex model＂ step to estimate several volatility models with time-varying higher-moments, and then we calculate VaR and ES val- ues according to Gram-Charlier extension distribution. We also back-test VaR based on unconditional coverage test and ES based on bootstrap. In spite of nice properties of the models with time-varying higher-moments, the backtesting results generally support models with static higher-moments.

作者王鹏

机构地区西南财经大学金融学院

出处《管理科学学报》 CSSCI 北大核心 2013年第2期33-45,94,共14页 Journal of Management Sciences in China

基金国家自然科学基金资助项目(71101119) 中央高校基本科研业务费专项资金资助项目(JBK120208)

关键词时变高阶矩 VAR ES Gram—Charlier扩展分布后验分析 time-varying higher-moments value-at-risk excepted shortfall gram-charlier expansion distribution backtesting

分类号 F830.9 [经济管理—金融学]

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