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Robust outlier detection for Asia–Pacific stock index returns

Ané, Thierry; Ureche-Rangau, Loredana; Gambet, Jean-Benoît; Bouverot, Julien (2008), Robust outlier detection for Asia–Pacific stock index returns, Journal of International Financial Markets, Institutions and Money, 18, 4, p. 326-343. http://dx.doi.org/10.1016/j.intfin.2007.03.001

Type
Article accepté pour publication ou publié
Date
2008
Journal name
Journal of International Financial Markets, Institutions and Money
Volume
18
Number
4
Publisher
Elsevier
Pages
326-343
Publication identifier
http://dx.doi.org/10.1016/j.intfin.2007.03.001
Metadata
Show full item record
Author(s)
Ané, Thierry

Ureche-Rangau, Loredana
IESEG Business School
Gambet, Jean-Benoît

Bouverot, Julien
Abstract (EN)
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.
Subjects / Keywords
Outliers; GARCH; Volatility forecast
JEL
C13 - Estimation: General
C15 - Statistical Simulation Methods: General
C51 - Model Construction and Estimation

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