Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models

Marco Barassi, Lajos Horvath and Yuqian Zhao

MPRA Paper from University Library of Munich, Germany

Abstract: We propose semi-parametric CUSUM tests to detect a change point in the correlation structures of non--linear multivariate models with dynamically evolving volatilities. The asymptotic distributions of the proposed statistics are derived under mild conditions. We discuss the applicability of our method to the most often used models, including constant conditional correlation (CCC), dynamic conditional correlation (DCC), BEKK, corrected DCC and factor models. Our simulations show that, our tests have good size and power properties. Also, even though the near--unit root property distorts the size and power of tests, de--volatizing the data by means of appropriate multivariate volatility models can correct such distortions. We apply the semi--parametric CUSUM tests in the attempt to date the occurrence of financial contagion from the U.S. to emerging markets worldwide during the great recession.

Keywords: Change point detection; Time varying correlation structure; Volatility processes; Monte Carlo simulation; Contagion effect (search for similar items in EconPapers)
JEL-codes: C12 C14 C32 G10 G15 (search for similar items in EconPapers)
Date: 2018-07-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Journal Article: Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models (2020)
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