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A Study on Outlier Detection Method for Financial Time Series Data
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 Title & Authors
A Study on Outlier Detection Method for Financial Time Series Data
Ha, M.H.; Kim, S.;
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 Abstract
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
 Keywords
Outliers;GARCH model;KOSPI data;
 Language
Korean
 Cited by
1.
이상 트래픽 탐지를 위한 로버스트 추정 방법 비교 연구,정재윤;김삼용;

Communications for Statistical Applications and Methods, 2011. vol.18. 4, pp.517-525 crossref(new window)
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 References
1.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. crossref(new window)

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. crossref(new window)

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. crossref(new window)