Value-at-Risk Estimation of the KOSPI Returns by Employing Long-Memory Volatility Models

被引：1

作者：

Oh, Jeongjun ^{[1
]}

Kim, Sunggon ^{[1
]}

机构：

[1] Univ Seoul, Dept Stat, 163 Siripdaero, Seoul 130743, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2013年 / 26卷 / 01期

关键词：

VaR estimation; long-memory; volatility model; FIGARCH; FIEGARCH;

D O I：

10.5351/KJAS.2013.26.1.163

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, we investigate the need to employ long-memory volatility models in terms of Value-at-Risk(VaR)estimation. We estimate the VaR of the KOSPI returns using long-memory volatility models such as FIGARCH and FIEGARCH; in addition, via back-testing we compare the performance of the obtained VaR with short memory processes such as GARCH and EGARCH. Back-testing says that there exists a long-memory property in the volatility process of KOSPI returns and that it is essential to employ long-memory volatility models for the right estimation of VaR.

引用

页码：163 / 185

页数：23

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