Comparison and Forecasting of VaR Models for Measuring Financial Risk: Evidence from China

Author:

Wang Yuling1ORCID,Xiang Yunshuang1,Zhang Huan2

Affiliation:

1. School of Economics, South-Central University for Nationalities, Wuhan 430074, China

2. School of Economics, Jinan University, Guangzhou 510632, China

Abstract

With increasing extremal risk, VaR has been becoming a popular methodology because it is easy to interpret and calculate. For comparing the performance of extant VaR models, this paper makes an empirical analysis of five VaR models: simple VaR, VaR based on RiskMetrics, VaR based on different distributions of GARCH-N, GARCH-GED, and GARCH-t. We exploit the daily closing prices of the Shanghai Composite Index from January 4, 2010, to April 8, 2020, and divide the entire sample into two periods for empirical analysis. The rolling window is used to update the daily estimation of risk. Based on the failure rates under different significance levels, we test whether a specific VaR model passes the back-testing. The results indicate that all models, except the RiskMetrics model, pass the test at a 5% level. According to the ideal failure rate, only the GARCH-GED model can pass the test at a 1% level. For the Kupiec confidence interval, the GARCH-t model can also pass the back-testing at all aforementioned levels. Particularly, we find that the GARCH-GED model has the lowest forecasting failure rate in the class of GARCH models.

Funder

South-Central University for Nationalities

Publisher

Hindawi Limited

Subject

Modeling and Simulation

Reference34 articles.

1. Systemic risk measurement: Multivariate GARCH estimation of CoVaR

2. Bayesian forecasting of Value-at-Risk based on variant smooth transition heteroskedastic models

3. The Dynamic Relationship between Crude Oil Prices and Stock Market Price Volatility in Nigeria: A Cointegrated VAR-GARCH Model

4. Financial market risk measurement model VAR;C. Wang;Journal of Systems Engineering,2000

5. VAR model and its influence on bank capital requirements;S. K. Peng;Statistics and decision,2007

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3