Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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리스크 관리 측면에서 살펴본 다변량 GARCH 모형 선택

On multivariate GARCH model selection based on risk management

  • 박세린 (성균관대학교 통계학과) ;
  • 백창룡 (성균관대학교 통계학과)
  • Park, SeRin (Department of Statistics, Sungkyunkwan University) ;
  • Baek, Changryong (Department of Statistics, Sungkyunkwan University)
  • 투고 : 2014.09.02
  • 심사 : 2014.10.12
  • 발행 : 2014.11.30
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초록

본 연구는 일변량 금융지수의 변동성 모형에서 GARCH(1,1) 모형이 여러 복잡한 GARCH 확장 모형에 비교해서 결코 뒤쳐지지 않는다는 Hansen과 Lunde (2005) 연구를 다변량 변동성으로 확장한다. 또한 모형의 비교 방법으로 예측값에 기반한 평균제곱예측오차 (MSPE) 뿐 만 아니라 리스크 관리 측면에서 최대 손실 금액을 나타내는 VaR 및 사후 검정인 실패율을 동시에 고려하였다. 모의실험 결과 다변량 변동성의 경우에서도 GARCH 모형이 예측력은 크게 다르지는 않았으나 리스크 관리 측면에서는 좀 더 신중한 판단을 요구함을 보인다. 또한 최근 10년동안의 KOSPI, NASDAQ 및 HANG SENG의 주가 지수 실증 자료를 통하여 리스크 관리 측면에서의 다변량 GARCH 모형 선택에 대해서 논의한다.

Hansen and Lund (2005) documented that a univariate GARCH(1,1) model is no worse than other sophisticated GARCH models in terms of prediction errors such as MSPE and MAE. Here, we extend Hansen and Lund (2005) by considering multivariate GARCH models and incorporating risk management measures such as VaR and fail percentage. Our Monte Carlo simulations study shows that multivariate GARCH(1,1) model also performs well compared to asymmetric GARCH models. However, we suggest that actual model selection should be done with care in light of risk management. It is applied to the realized volatilities of KOSPI, NASDAQ and HANG SENG index for recent 10 years.

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참고문헌

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