응용통계연구 (The Korean Journal of Applied Statistics)

  • 제19권3호
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  • Pages.481-504
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  • 2006
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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코퓰러과 극단치이론을 이용한 위험척도의 추정 및 성과분석

Estimation and Performance Analysis of Risk Measures using Copula and Extreme Value Theory

  • 여성칠 (건국대학교 상경대학 응용통계학과)
  • Yeo, Sung-Chil (Department of Applied Statistics, Konkuk University)
  • 발행 : 2006.11.30
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⟨ 이전 논문 다음 논문 ⟩

초록

금융위험의 측정 및 관리를 위한 도구로서 분포의 꼬리 부분과 관련한 위험척도로 VaR가 현재 널리 활용되고 있다. 특히 VaR의 정확한 추정을 위해 정규분포를 가정한 기존의 방법보다는 극단치이론을 이용한 방법이 최근 관심을 끌고 있다. 지금까지 극단치이론을 이용한VaR의 추정에 관한 연구는 대부분 단변량의 경우에 대해 이루어졌다. 본 논문에서는 코퓰러를 극단치이론에 결부시켜 다변량 극단치분포를 모형화하여 포트폴리오 위험측정을 다루고 있다. 특히 본 연구에서는 포트폴리오 위험 척도로 VaR와 더불어 ES에 대한 추정 방법도 함께 논의하였다. 포트폴리오 위험측정을 위한 방법으로 본 논문에서 논의한 코퓰러-극단치이론에 의한 접근방법이 기존의 분산-공분산 방법보다 상대적으로 우수한지를 실증자료에 대한 사후검증을 통해 살펴보았다.

VaR, a tail-related risk measure is now widely used as a tool for a measurement and a management of financial risks. For more accurate measurement of VaR, recently we are particularly concerned about the approach based on extreme value theory rather than the traditional method based on the assumption of normal distribution. However, many studies about the approaches using extreme value theory was done only for the univariate case. In this paper, we discuss portfolio risk measurements with modelling multivariate extreme value distributions by combining copulas and extreme value theory. We also discuss the estimation of ES together with VaR as portfolio risk measures. Finally, we investigate the relative superiority of EVT-copula approach than variance-covariance method through the back-testing of an empirical data.

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피인용 문헌

  1. VaR Estimation with Multiple Copula Functions vol.24, pp.5, 2011, https://doi.org/10.5351/KJAS.2011.24.5.809
  2. Performance analysis of EVT-GARCH-Copula models for estimating portfolio Value at Risk vol.29, pp.4, 2016, https://doi.org/10.5351/KJAS.2016.29.4.753