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Performance Analysis of Volatility Models for Estimating Portfolio Value at Risk

포트폴리오 VaR 측정을 위한 변동성 모형의 성과분석

  • Yeo, Sung Chil (Department of Applied Statistics, Konkuk University) ;
  • Li, Zhaojing (Department of Applied Statistics, Konkuk University)
  • 여성칠 (건국대학교 응용통계학과) ;
  • 이조청 (건국대학교 응용통계학과)
  • Received : 2015.03.30
  • Accepted : 2015.04.15
  • Published : 2015.06.30

Abstract

VaR is now widely used as an important tool to evaluate and manage financial risks. In particular, it is important to select an appropriate volatility model for the rate of return of financial assets. In this study, both univariate and multivariate models are considered to evaluate VaR of the portfolio composed of KOSPI, Hang-Seng, Nikkei indexes, and their performances are compared through back testing techniques. Overall, multivariate models are shown to be more appropriate than univariate models to estimate the portfolio VaR, in particular DCC and ADCC models are shown to be more superior than others.

VaR는 금융위험을 측정하고 관리하기위한 중요한 도구로 현재 널리 사용되고 있다. 특히 금융자산 수익률의 변동성에 적합한 모형을 찾는 것은 VaR의 정확한 측정을 위해 중요한 과제이다. 본 연구에서는 한국의 코스피, 중국의 항셍, 일본의 니케이지수들로 구성된 포트폴리오의 VaR를 측정하기 위한 변동성모형으로 다양한 일변량모형들과 다변량모형들을 함께 고려하여 그 성과를 비교하였다. 사후검증을 통해 전체적으로 일변량모형들보다는 다변량모형들이 VaR의 측정에 더 적합한 것으로 보여 졌으며 특히 DCC와 ADCC모형이 더욱 우수한 것으로 나타났다.

Keywords

Acknowledgement

Supported by : 건국대학교

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