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

Marco Barassi, Lajos Horvath and Yuqian Zhao

Journal of Business & Economic Statistics, 2020, vol. 38, issue 2, 340-349

Abstract: We propose semiparametric CUSUM tests to detect a change-point in the correlation structures of nonlinear 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 semiparametric CUSUM tests in the attempt to date the occurrence of financial contagion from the US to emerging markets worldwide during the great recession. Supplementary materials for this article are available online.

Date: 2020
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Working Paper: Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:38:y:2020:i:2:p:340-349

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DOI: 10.1080/07350015.2018.1505630

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