Detecting Multiple Breaks in Financial Market Volatility Dynamics

Detecting Multiple Breaks in
Financial Market Volatility Dynamics*

Elena Andreou

University of Cyprus

Eric Ghysels

University of North Carolina and CIRANO

First draft: October 22, 2001
This version: February 20, 2002

Abstract

The paper evaluates the performance of several recently proposed tests
for structural breaks in conditional variance dynamics of asset returns. The
tests apply to the class of ARCH and SV type processes as well as data-
driven volatility estimators using high-frequency data. In addition to testing
for the presence of breaks, the statistics identify the number and location of
multiple breaks. We study the size and power of the new tests for detecting
breaks in the second conditional variance under various realistic univariate
heteroskedastic models, change-point hypotheses 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. These events resulted in changes in the dynamics of
volatility of asset returns in the samples prior and post the breaks.

JEL Classification: GIO, C15, C13.

Key Words: change-point, break dates, ARCH, high-frequency data.

*We would like to thank Marine Carrasco, Piotr Kokoszka and two anonymous referees
for helpful comments.

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