Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Combination of Value-at-Risk Models with Support Vector Machine

서포트벡터기계를 이용한 VaR 모형의 결합

  • 김용태 (단국대학교 정보통계학과) ;
  • 심주용 (대구가톨릭대학교 응용통계학과) ;
  • 이장택 (단국대학교 정보통계학과) ;
  • 황창하 (단국대학교 정보통계학과)
  • Published : 2009.09.30
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Abstract

Value-at-Risk(VaR) has been used as an important tool to measure the market risk. However, the selection of the VaR models is controversial. This paper proposes VaR forecast combinations using support vector machine quantile regression instead of selecting a single model out of historical simulation and GARCH.

VaR(Value-at-Risk)는 시장위험을 측정하기 위한 중요한 도구로 사용되고 있다. 그러나 적절한 VaR 모형의 선택에는 논란의 여지가 많다. 본 논문에서는 특정 모형을 선택하여 VaR 예측값을 구하는 대신 대표적으로 많이 사용되는 두개의 VaR 모형인 역사적 모의실험과 GARCH 모형의 예측값들을 서포트벡터기계 분위수 회귀모형을 이용하여 결합하는 방법을 제안한다.

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