东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2008, Vol. 29 ›› Issue (4): 601-604.DOI: -

• 论著 • 上一篇    下一篇

GARCH-EVT模型在动态VaR中的应用

高莹;周鑫;金秀;   

  1. 东北大学工商管理学院;东北大学工商管理学院;东北大学工商管理学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2008-04-15 发布日期:2013-06-22
  • 通讯作者: Gao, Y.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(70771023)

Application of GARCH-EVT model in dynamic VaR

Gao, Ying (1); Zhou, Xin (1); Jin, Xiu (1)   

  1. (1) School of Business Administration, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2008-04-15 Published:2013-06-22
  • Contact: Gao, Y.
  • About author:-
  • Supported by:
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摘要： 在综合考虑金融资产收益数据分布的波动集群性和厚尾特征,尤其是波动的条件异方差对动态VaR估计的影响的基础上,运用极值理论(EVT),建立了GARCH-EVT模型,计算了上海证券市场综合指数的动态VaR,并且将GARCH-EVT模型与GARCH-NORMAL模型进行比较.通过实证分析,并利用后验测试,结果表明GARCH-EVT模型优于GARCH-NORMAL模型.GARCH-EVT模型很好地解决了波动集群性和厚尾现象,为管理者和投资者提供了一个控制风险、预测收益的量化工具.

关键词: 动态VaR(valueatrisk), GARCH, 极值理论, 波动, 厚尾

Abstract: Considering both the characteristics of clustering volatility and fat-tail of the data distribution of returns on financial assets especially the impact of conditional heteroscedaticity on the estimate of dynamic VAR, a GARCH-EVT model is developed by EVT (extreme value theory) to calculate the dynamic VAR(value at risk) of SSCI (Shanghai stock comprehensive index), then the model is compared with the GARCH-NORMAL model. The empirical analysis and posterior test results reveal that the GARCH-EVT model is superior to the GARCH-NORMAL model, because the former can solve better the problems of clustering volatility and fat-tail phenomenon. So it provides the managers and investors with quantitatively useful means for risk control.

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