兰州大学机构库 >数学与统计学院
基于AR-GARCH-EVT模型的CVaR估计及应用
Alternative TitleEstimating and Application of CVaR Based on the AR-GARCH-EVT Model
谢绍魁
Thesis Advisor严定琪
2013-05-26
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
KeywordCVaR EVT 广义帕累托分布 阀值 POT模型 风险控制
Abstract  针对传统的风险测量方法VaR在可加性、凸性等方面的缺陷,本文引入了新的金融风险度量的工具——CVaR,它弥补了VaR对损失超过阀值点(VaR)的部分未做任何处理的缺点。同动态VaR的研究方法类似,CVaR的研究也可以通过以下两个方面分析:一方面是风险度量模型,该模型一般会假设损益分布服从正态分布,但这样就忽略了实际中金融数据呈现出的尖峰厚尾性和极端性;另一方面,从实际金融数据的波动特征出发,构建恰当的波动模型。   本文采用GARCH类模型解决金融时间序列的集群波动性,从而拟合历史数据的动态条件均值和条件方差。鉴于极值理论估计分布厚尾特性的优势,本文用POT模型处理金融资产收益序列数据的尾部。接下来引入CVaR的概念,将GARCH类模型,POT模型和CVaR结合起来,形成本文最终提出的一般性AR-GARCH-EVT-CVaR模型。 对于模型的实用性与有效性,本文通过拟合上证指数3132个历史数据进行了实际验证。经过计算与失败频率检验,并与传统的VaR模型进行了比较,发现本文所提出的方法给出了比VaR更好的一天期的CVaR估计。
Other Abstract   In this paper,for the lack of the additivity ,convexity and etc in traditional risk measurement methods, we introduces a new financial risk measurement tool:CVaR. It makes up for the VaR to do nothing about the part of over the threshold point (VaR) . Relative to the research of dynamic VaR,there are two aspects about the analysis of CVaR: on the one hand, it is the risk measurement model, which usually assumes that the profit and loss distribution is normal distribution, but it ignores the actual financial data showing a spike in thick tail and extreme; on the other hand,from the volatility of actual financial data, we can build appropriate volatility model.    We solve the clustering of financial time series volatility by GARCH models, and calculate the dynamic condition means and variances of historical data. For the advantage of the extreme value theory to estimate fat-tailed distribution, this paper deal with the tail end of the financial assets yield sequence using the POT model. Then we introduce the concept of CVaR, and use the combination of GARCH models, POT and CVaR model in order to finally put forward the general AR - GARCH - EVT - CVaR model in the paper. For testing the practicality and effectiveness of this model, this article make the actual test and verify by fitting the Shanghai index 3132 historical data. Through calculation and testing failure frequency, and comparing the results with the traditional VaR model, we find that the proposed method of our paper gives a better 1-day CVaR-estimation than traditional VaR estimated.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225260
Collection数学与统计学院
Recommended Citation
GB/T 7714
谢绍魁. 基于AR-GARCH-EVT模型的CVaR估计及应用[D]. 兰州. 兰州大学,2013.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[谢绍魁]'s Articles
Baidu academic
Similar articles in Baidu academic
[谢绍魁]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[谢绍魁]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.