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Detecting Mutiple Breaks in Financial Market Volatility Dynamics

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Author Info
Elena Andreou
Eric Ghysels ()

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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.

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Paper provided by CIRANO in its series CIRANO Working Papers with number 2001s-65.

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Date of creation: 01 Nov 2001
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Handle: RePEc:cir:cirwor:2001s-65

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Keywords: Structural breaks ARCH long memory high-frequency data Ruptures structurelles ARCH mémoire longue données à haute fréquence

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Find related papers by JEL classification:
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation

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  1. Helena Veiga, 2006. "Are Feedback Factors Important In Modelling Financial Data?," Statistics and Econometrics Working Papers ws060101, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
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  2. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series CESifo Working Paper No. , CESifo GmbH. [Downloadable!]
  3. Cizek, P. & Haerdle, W. & Spokoiny, V., 2007. "Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models," Discussion Paper 2007-35, Tilburg University, Center for Economic Research. [Downloadable!]
  4. Stapf, Jelena & Werner, Thomas, 2003. "How wacky is the DAX? The changing structure of German stock market volatility," Discussion Paper Series 1: Economic Studies 2003,18, Deutsche Bundesbank, Research Centre. [Downloadable!]
  5. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO. [Downloadable!]
  6. D van Dijk & D R Osborn & M Sensier, 2004. "Testing for causality in variance in the presence of breaks," Centre for Growth and Business Cycle Research Discussion Paper Series 45, Economics, The Univeristy of Manchester. [Downloadable!]
    Other versions:
  7. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute. [Downloadable!]
  8. Pavel Cizek & Wolfgang Härdle & Vladimir Spokoiny, 2008. "Adaptive pointwise estimation in time-inhomogeneous time-series models," SFB 649 Discussion Papers SFB649DP2008-002, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
  9. Elena Andreou & Eric Ghysels, 2003. "Test for Breaks in the Conditional Co-Movements of Asset Returns," University of Cyprus Working Papers in Economics 3-2003, University of Cyprus Department of Economics. [Downloadable!]
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  10. Eric Hillebrand & Gunther Schnabl, 2004. "The Effects of Japanese Foreign Exchange Intervention, GARCH Estimation and Change Point Detection," Money Macro and Finance (MMF) Research Group Conference 2004 7, Money Macro and Finance Research Group. [Downloadable!]
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  11. Eric Hillebrand & Gunther Schnabl, 2006. "A structural break in the effects of Japanese foreign exchange intervention on yen/dollar exchange rate volatility," Working Paper Series 650, European Central Bank. [Downloadable!]
  12. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO. [Downloadable!]
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  14. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics. [Downloadable!]
  15. Andreu Sansó & Vicent Aragó & Josep Lluís Carrion, 2003. "Testing for Changes in the Unconditional Variance of Financial Time Series," DEA Working Papers 5, Universitat de les Illes Balears, Departament d'Economía Aplicada. [Downloadable!]
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