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Prediction of Conditional Variance under GARCH Model Based on Bootstrap Methods

붓스트랩 방법을 이용한 일반화 자기회귀 조건부 이분산모형에서의 조건부 분산 예측

  • 김희영 (고려대학교 의학통계학교실) ;
  • 박만식 (고려대학교 의학통계학교실)
  • Published : 2009.03.30

Abstract

In terms of generalized autoregressive conditional heteroscedastic(GARCH) model, estimation of prediction interval based on likelihood is quite sensitive to distribution of error. Moveover, it is not an easy job to construct prediction interval for conditional variance. Recent studies show that the bootstrap method can be one of the alternatives for solving the problems. In this paper, we introduced the bootstrap approach proposed by Pascual et al. (2006). We employed it to Korean stock price data set.

References

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