A Study on Outlier Detection Method for Financial Time Series Data

재무 시계열 자료의 이상치 탐지 기법 연구

  • Received : 20091200
  • Accepted : 20100100
  • Published : 2010.02.28


In this paper, we show the performance evaluation of outlier detection methods based on the GARCH model. We first introduce GARCH model and the methods of outlier detection in the GARCH model. The results of small simulation and the real KOSPI data show the out-performance of the outlier detection method over the traditional method in the GARCH model.


Outliers;GARCH model;KOSPI data


  1. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327.
  2. Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, Holden Day, San Francisco.
  3. Charles, A. and Darne, O. (2006). Outliers and GARCH models in financial data, Journal of Economics Letters, 86, 347-352.
  4. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation, Econometrica, 50, 987-1007.
  5. Fox, A. J. (1972). Outliers in time series, Journal of Royal Statistical Society B, 34, 350-363.
  6. Franses, P. H. and Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility, International Journal of Forecasting, 15, 1-9.

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