Change-point detection in the conditional correlation structure of multivariate volatility models

Marco Barassi, Lajos Horvath, Yuqian Zhao

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We propose semi-parametric CUSUM tests to detect a change point in the correlation structures of non–linear multivariate models with dy- namically 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 appro- priate 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.
Original languageEnglish
Pages (from-to)340-349
Number of pages10
JournalJournal of Business and Economic Statistics
Issue number2
Early online date6 Aug 2018
Publication statusPublished - 2 Apr 2020


  • Change point detection
  • Contagion effect
  • Monte Carlo simulation
  • Time varying correlation structure
  • Volatility processes


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