摘要
在金融时间序列研究中,随着近年来全球金融危机的频繁发生,越来越多的学者开始考虑用极值理论的方法来进行金融风险管理的研究.笔者主要运用广义极值分布来分析我国A股市场,并借助极大似然估计法求解参数;同时考虑改进极值理论关于时间序列的独立性假设条件,建立更加符合实际的平稳时间序列VaR度量模型;最后将该方法运用于沪深300日收益率的风险估计.研究表明:改进后的模型能够更加准确地刻画实际市场的极端波动情况,弥补了传统极值理论低估风险的不足,从而为机构投资者如何以最少保证金来防范金融危机,提供了一个更有利的工具.
Extreme value theory is increasingly used to study financial risk management in the research area of financial time series. In this paper, general extreme value （GEV） distribution is mainly employed to analyze A share market, and maximum likelihood is used to estimate the unknown parameters. On the other hand, the assumption of independence in extreme value theory is critical in practice. Taking it into account, a modified VaR measuring model is also constructed and applied to estimate the risk of HS300 Stock Index. The modified model is shown to more precisely describe the extreme fluctuations in the actuarial market, which compensates for the underestimate of risk in traditional extreme value theory. It provides a more useful instrument for institutional investors to make profit at least margin transaction guarantee sum.
出处
《浙江工业大学学报》
CAS
2013年第5期578-582,共5页
Journal of Zhejiang University of Technology
基金
国家自然科学基金资助项目(11126211)
浙江省自然科学基金资助项目(Q12A010082)
浙江省教育厅基金资助项目(Y201121764)
关键词
极值理论
极值指标
GEV分布
VaR度量模型
extreme value theory
extremal index
GEV distribution
VaR measuring model
