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The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests

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Elena Andreou
Eric Ghysels ()

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Abstract

The paper evaluates the performance of several recently proposed change-point tests applied to conditional variance dynamics and conditional distributions of asset returns. These are CUSUM-type tests for beta-mixing processes and EDF-based tests for the residuals of such nonlinear dependent processes. Hence the tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. It is shown that some of the high-frequency volatility estimators substantially improve the power of the structural breaks tests especially for detecting changes in the tail of the conditional distribution. Similarly, certain types of filtering and transformation of the returns process can improve the power of CUSUM statistics. We also explore the impact of sampling frequency on each of the test statistics.

Ce papier évalue la performance de plusieurs tests de changement structurel CUSUM et EDF pour la structure dynamique de la variance conditionelle et de la distribution conditionnelle. Nous étudions l'impact 1) de la fréquence des observations, 2) de l'utilisation des données de haute fréquence pour le calcul des variances conditionnelles et 3) de transformation des séries pour améliorer la puissance des tests.

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Paper provided by CIRANO in its series CIRANO Working Papers with number 2004s-25.

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Date of creation: 01 May 2004
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Handle: RePEc:cir:cirwor:2004s-25

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Keywords: Change-point tests; CUSUM; Kolmogorov-Smirnov; GARCH; quadratic variation; power variation; high-frequency data; location-scale distribution family; tests de changement structurel; CUSUM; Kolmogov-Smirnov; GARCH; variation quadratique; 'power variation'; données de haute fréquence;

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Power and bipower variation with stochastic volatility and jumps," Economics Papers 2003-W17, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
    Other versions:
  2. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO. [Downloadable!]
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