文档介绍：华中科技大学硕士学位论文
摘要
随着大部分金融机构组合交易资产数目的剧增,金融市场呈现出前所未有的波
动性和脆弱性,市场风险成为金融风险的主要形式,使得金融监管当局和金融机构
不断强化市场风险的管理与监督。VaR 风险计量方法自 1993 年提出以来,已成为
金融机构和金融管理机构衡量市场风险的标准方法。
计算 VaR 的方法有很多,但它们都各有缺陷,几乎所有传统方法的观测值都集
中在分布的中部,实际上风险评估最关注的是分布的尾部。分布尾部的点都是一些
极少发生又具有显著影响的观测值,称其为极值,极值理论正是对这些极值提供统
计分析的模型。本文介绍了极值方法的理论基础,运用 POT 模型和混合方法进行
建模,并通过上证指数对模型进行了实证分析,分析表明极值理论能很好的刻画金
融回报分布的尾部,得到较精确的 VaR 估计值。
金融时间序列带有一些明显的特性,如尖峰厚尾性、不服从正态分布、具有杠
杆效应等,故在风险度量中所选的模型必须要能反映这些特征,其度量的结果才会
准确,GARCH 模型类正好能满足这一要求。每种模型都存在缺陷,GARCH 模型
也不例外,它的缺点是只依赖最新的样本收益数据去推断均值方差,而忽略历史样
本收益对未来均值方差的影响。此外,本文还介绍了期权价值的风险度量,对股票
期权的风险进行了一些有益的研究,这对于投资者进一步了解股票期权,及期权的
发展、完善有一定帮助。
关键词:风险度量,VaR,极值理论,POT 模型,股票期权,GARCH 模型类
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华中科技大学硕士学位论文
Abstract
The large increase in the number of traded assets in the portfolio of most financial
institutions has made the measurement of market risk a primary concern for regulators
and for internal risk control. After being proposed in 1993, VaR approach has e the
standard for risk management industry.
Though VaR has pute methods, they have limitations. Almost all of the
traditional methods estimating tail-related risk VaR focus on the central observations or,
in other words, on returns under normal market conditions. However, VaR are risk
measures that relates solely to the tails of the distribution. The extreme values which lies
in the tail are some rarely happened events that have significant influence. Extreme
Value Theory(EVT) is the statistical model to study the behavior of extreme values. This
paper introduces the basic knowledge of EVT and estimates VaR using VaR. The
applications methods of EVT have POT model and mixing methods. And based on the
stock date of Shanghai Securities Exchange, this paper makes an empirical analysis of
VaR estimation. Empirical findings conclude the EVT can well approximate the tail of
financial return distribution.
Financial dates analysis shows that the return rates distribution is fat-tailed and
doesn’t obey nor