Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Estimation of nonlinear GARCH-M model

비선형 평균 일반화 이분산 자기회귀모형의 추정

  • Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu) ;
  • Lee, Jang-Taek (Department of Statistics, Dankook University)
  • 심주용 (대구가톨릭대학교 응용통계학과) ;
  • 이장택 (단국대학교 정보통계학과)
  • Received : 2010.05.13
  • Accepted : 2010.08.28
  • Published : 2010.09.30
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Abstract

Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

최소제곱 서포트벡터기계는 비선형회귀분석과 분류에 널리 쓰이는 커널기법이다. 본 논문에서는 금융시계열자료의 평균 및 변동성을 추정하기 위하여 평균의 추정 방법으로는 가중최소제곱 서포트벡터기계, 변동성의 추정 방법으로는 최소제곱 서포트벡터기계를 사용하는 비선형 평균 일반화 이분산 자기회귀모형을 제안한다. 제안된 모형은 선형 일반화 이분산 자기회귀모형 및 선형 평균 일반화 이분산 자기회귀모형보다 더 나은 추정 능력을 가진다는 것을 실제자료의 추정을 통하여 보였다.

Keywords

References

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