The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Analysis of Multivariate-GARCH via DCC Modelling

DCC 모델링을 이용한 다변량-GARCH 모형의 분석 및 응용

  • Choi, S.M. (Department of Statistics, Sookmyung Women's University) ;
  • Hong, S.Y. (Department of Statistics, Sookmyung Women's University) ;
  • Choi, M.S. (Department of Statistics, Sookmyung Women's University) ;
  • Park, J.A. (Department of Statistics, Sookmyung Women's University) ;
  • Baek, J.S. (Department of Statistics, Sookmyung Women's University) ;
  • Hwang, S.Y. (Department of Statistics, Sookmyung Women's University)
  • 최성미 (숙명여자대학교 통계학과) ;
  • 홍선영 (숙명여자대학교 통계학과) ;
  • 최문선 (숙명여자대학교 통계학과) ;
  • 박진아 (숙명여자대학교 통계학과) ;
  • 백지선 (숙명여자대학교 통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Published : 2009.10.31
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Abstract

Conditional correlation between financial time series plays an important role in risk management, asset allocation and portfolio selection and therefore diverse efforts for modeling conditional correlations in multivariate-GARCH processes have been made in last two decades. In particular, CCC (cf. Bollerslev, 1990) and DCC(dynamic conditional correlation, cf. Engle, 2002) models have been commonly used since they are relatively parsimonious in the number of parameters involved. This article is concerned with DCC modeling for multivariate GARCH processes in comparison with CCC specification. Various multivariate financial time series are analysed to illustrate possible advantages of DCC over CCC modeling.

금융 시계열 자료들 간의 상관계수는 자산의 배분, 위험관리 그리고 포트폴리오의 선택에 있어서 중요한 역할을 한다. 이러한 상관계수들을 모형화하기 위해 단변량-GARCH 모형을 다변량-GARCH 모형으로 확장시킨 MGARCH류 모형들에 대한 많은 연구들이 진행되고 있다. 특히, CCC 모형 (Bollerslev, 1990)과 DCC 모형 (Engle, 2002)은 다른 모형들에 비해 추정해야 할 모수의 수가 작다는 이점으로 인해 분석에 널리 쓰이고 있다. 본 논문에서는 국내 주가자료에 대해 CCC 모형과 DCC 모형을 적합시킨 후, 각 모형들에 대한 VaR(value at risk)와 사후검증(back-testing), 결합예측영역(joint prediction region) 등을 통하여 두 모형의 예측 능력을 비교해 보고자 한다.

Keywords

References

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