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Robust outlier detection for Asia-Pacific stock index returns

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Author Info

  • Ané, Thierry
  • Ureche-Rangau, Loredana
  • Gambet, Jean-Benoît
  • Bouverot, Julien

Abstract

Outliers can lead to model misspecifications, poor forecasts and invalid inferences. Their identification and correction is therefore an important objective of financial modeling. This paper introduces a simple method to detect outliers in a financial series. It uses an AR(1)-GARCH(1,1) model to calculate interval forecasts for one-step ahead returns that are then compared to realized returns to determine whether or not we are in the presence of an aberrant observation. The GARCH model, however, is only used as a filter and the identification algorithm remains robust to model misspecifications. The efficiency of this outlier-correction technique is first tested with a simulation study, before being applied to five Asian stock market returns to identify the outlying observations. After an analysis of these extreme fluctuations, the out-of-sample forecasting performance of our outlier-corrected model is then compared to the classical forecasts of a GARCH model in which no account is taken of outliers.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of International Financial Markets, Institutions and Money.

Volume (Year): 18 (2008)
Issue (Month): 4 (October)
Pages: 326-343

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Handle: RePEc:eee:intfin:v:18:y:2008:i:4:p:326-343

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Web page: http://www.elsevier.com/locate/intfin

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References

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Cited by:
  1. F. Javier Trivez & Beatriz Catalan, 2009. "Detecting level shifts in ARMA-GARCH (1,1) Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 679-697.
  2. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
  3. Loredana Ureche-Rangau & Franck Speeg, 2011. "A simple method for variance shift detection at unknown time points," Economics Bulletin, AccessEcon, vol. 31(3), pages 2204-2218.

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