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Detecting Mutiple Breaks in Financial Market Volatility Dynamics

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  • Elena Andreou
  • Eric Ghysels

Abstract

We apply several recently proposed tests for structural breaks in conditional variance and covariance dynamics. The tests apply to both the class of ARCH and SV type processes and allow for long memory features. We also apply them to data-driven volatility estimators using high-frequency data and suggest multivariate applications. In addition to testing for the presence of breaks, the statistics allow to identify the number of breaks and the location of multiple breaks. We study the size and power of the new tests under various realistic univariate and multivariate conditional variance models and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. We find changes in the dynamics and long memory of volatility in the samples prior and post the breaks. Nous appliquons plusieurs nouveaux tests conçus pour déceler les ruptures structurelles dans la dynamique de variance et de covariance conditionnelles. Les tests s'appliquent à la fois aux processus de la classe ARCH et de type SV et tiennent compte des caractéristiques de mémoire longue. Nous les appliquons également aux estimateurs de volatilité engendrés par les données, en utilisant des données à haute fréquence et nous suggérons des applications multivariées. En plus de déterminer la présence des ruptures, les statistiques permettent d identifier le nombre de ruptures ainsi que l'emplacement de ruptures multiples. Nous étudions la taille et la puissance des nouveaux tests pour divers modèles réalistes univariés et multivariés de variance conditionnelle et d échantillonnage. L article conclut avec une analyse empirique à partir de données provenant des marchés d actions et de taux de change pour lesquels nous trouvons de multiples ruptures associées aux crises financières asiatiques et russes. Dans les échantillons sélectionnés avant et après les ruptures, nous trouvons des changements dans la dynamique et dans la mémoire longue de la volatilité.

Suggested Citation

  • Elena Andreou & Eric Ghysels, 2001. "Detecting Mutiple Breaks in Financial Market Volatility Dynamics," CIRANO Working Papers 2001s-65, CIRANO.
    • Handle: RePEc:cir:cirwor:2001s-65
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    More about this item

    Keywords

    Structural breaks; ARCH; long memory; high-frequency data ; Ruptures structurelles; ARCH; mémoire longue; données à haute fréquence;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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