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Accueil du site > Séminaires > Probabilités Statistiques et réseaux de neurones > Change-point detection in GARCH models : asymptotic and bootstrap tests

Vendredi 27 mai 2005, à 11h15

Change-point detection in GARCH models : asymptotic and bootstrap tests

Gilles Teyssière (Samos, Université Paris 1)

Résumé : Two classes of tests designed to detect changes in volatility are proposed. Procedures based on squared model residuals and on the likelihood ratio are considered. The tests are applicable to parametric nonlinear models like GARCH. Both asymptotic and bootstrap tests are investigated by means of a simulation study and applied to returns data. The tests based on the likelihood ratio are shown to be generally preferable, and appear to have a better power that the semiparameteric and parametric competing tests for change—point detection. A wavelet based estimator of long memory is applied to returns data to shed light on the interplay of change—points and long memory.

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