응용통계연구 (The Korean Journal of Applied Statistics)

  • 제29권4호
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  • Pages.753-771
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  • 2016
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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포트폴리오 VaR 측정을 위한 EVT-GARCH-코퓰러 모형의 성과분석

Performance analysis of EVT-GARCH-Copula models for estimating portfolio Value at Risk

  • 이상훈 (건국대학교 응용통계학과) ;
  • 여성칠 (건국대학교 응용통계학과)
  • Lee, Sang Hun (Department of Applied Statistics, Konkuk University) ;
  • Yeo, Sung Chil (Department of Applied Statistics, Konkuk University)
  • 투고 : 2016.04.11
  • 심사 : 2016.05.25
  • 발행 : 2016.06.30
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초록

금융기관의 위험관리를 위한 중요한 도구로서 현재 VaR가 널리 사용되고 있다. 본 논문에서는 코퓰러 함수들을 이용하여 극단치이론과 GARCH 모형을 결합한 일변량분포로부터 구축한 다변량분포들을 바탕으로 코스피, 다우존스, 상하이 그리고 니케이 지수들로 구성된 포트폴리오의 VaR 추정과 그 성과에 관해 논의하였다. 사후검증 결과 전체적으로 볼 때 가우시안, t, 클레이톤, 프랭크 코퓰러를 사용한 t-분포의 오차항을 가진 변동성 모형들이 포트폴리오 VaR의 측정에 적합한 모형들로 나타났으며, 특히 프랭크 코퓰러의 경우에 가장 우수한 성과를 나타내었다.

Value at Risk (VaR) is widely used as an important tool for risk management of financial institutions. In this paper we discuss estimation and back testing for VaR of the portfolio composed of KOSPI, Dow Jones, Shanghai, Nikkei indexes. The copula functions are adopted to construct the multivariate distributions of portfolio components from marginal distributions that combine extreme value theory and GARCH models. Volatility models with t distribution of the error terms using Gaussian, t, Clayton and Frank copula functions are shown to be more appropriate than the other models, in particular the model using the Frank copula is shown to be the best.

키워드

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