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基于双曲线记忆HYGARCH模型的动态风险VaR测度能力研究

林宇   

  1. 成都理工大学商学院, 四川成都 610059
  • 收稿日期:2010-03-15 修回日期:2011-11-09 出版日期:2011-12-30 发布日期:2011-12-30
  • 作者简介:林宇(1973- ),男(汉族),四川仪陇人,成都市成都理工大学商学院副教授,博士,研究方向:金融风险管理、金融工程。
  • 基金资助:
    国家自然科学基金资助项目(71171025,71071131,70771097);教育部“新世纪优秀人才支持计划”(NCET-08-0826);教育部人文社会科学研究青年基金(10YJCZH086);成都理工大学高层次人才科研启动基金(HJ0038);成都理工大学优秀创新团队培育计划项目(2010TD01)

Study on Dynamic VaR Measurement with Hyperbolic Memory GARCH

LIN Yu   

  1. Business School, Chengdu University of Technology, Chengdu 610059, China
  • Received:2010-03-15 Revised:2011-11-09 Online:2011-12-30 Published:2011-12-30

PDF (PC)

2158

被引次数

25

摘要： 本文针对金融市场的典型事实特征,运用自回归分数移动平均(Fractional Integrated Autoregressive Moving Average,ARFIMA)模型与双曲线记忆广义自回归条件异方差模型(Hyperbolic Memory Generalized Autoregressive Conditional Heteroscedasticity,HYGARCH)模型、分数协整非对称自回归条件异方差(Fractional Integrated Asymmetric Power Autoregressive Conditional Heteroscedasticity,FIAPARCH)模型和分数协整指数广义自回归条件异方差(Fractional Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity,FIEGARCH)模型结合,并运用有偏学生t分布(Skew Student t Distribution,SKST)来捕获金融收益分布形态,以此开展动态风险测度研究,进而运用返回测试(Back-Testing)中的似然比率测试(Likelihood Ratio Test,LRT)和动态分位数回归(Dynamic Quantile Regression,DQR)方法对风险模型的准确性与精度进行联合检验。通过实证研究,得到了一些非常有价值的实证结论:ARFIMA(1,d,1)-FIAPARCH(1,d,1)-SKST模型与ARFIMA(1,d,1)-HYGARCH(1,d,1)-SKST模型均表现出卓越的风险测度能力,但没有绝对优劣之分;ARFIMA(1,d,1)-FIEGARCH(1,d,1)-SKST模型在成熟市场的表现能力差强人意;本文引入的所有风险模型在中国大陆沪、深股市表现优越且没有实质性差异。

关键词: 金融市场, 典型事实, HYGARCH, 动态风险, 测度

Abstract: This paper applies Fractional Integrated Autoregressive Moving Average(ARFIMA)model and Hyperbolic Memory Generalized Autoregressive Conditional Heteroscedasticity(HYGARCH)model, Fractional Integrated Asymmetric Power Autoregressive Conditional Heteroscedasticity(FIAPARCH) model and Fractional Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity (FIEGARCH)model to capture some stylized facts of conditional volatility and conditional return of financial markets,and apply Skew Student t Distribution(SKST)to capture return distribution,and then measure dynamic risk of financial markets.At last,we use Likelihood Ratio Test(LRT)and Dynamic Quantile Regression(DQR)to test accuracy of risk measurement model as well.Our results show that all risk models used in this paper has no significant difference on accuracy for Chinese stock markets;ARFIMA(1,d,1)-FIAPARCH(1,d,1)-SKST model is no excel to ARFIMA(1,d,1)–HYGARCH(1,d,1)SKST model in developed market;ARFIMA(1,d,1)-FIEGARCH(1,d,1)-SKST model can not measure risk accurately for developed market.

Key words: financial markets, stylized facts, HYGARCH, dynamic risk, measurement

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