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

Author

Listed:
  • Marco Barassi
  • Lajos Horváth
  • Yuqian Zhao

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.

Suggested Citation

  • Marco Barassi & Lajos Horváth & Yuqian Zhao, 2020. "Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 340-349, April.
    • Handle: RePEc:taf:jnlbes:v:38:y:2020:i:2:p:340-349
    DOI: 10.1080/07350015.2018.1505630
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    Cited by:

    1. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    2. Lee, Sangyeol & Meintanis, Simos G. & Pretorius, Charl, 2022. "Monitoring procedures for strict stationarity based on the multivariate characteristic function," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    3. Li, Hemei & Liu, Zhenya & Xiao, Zhijie, 2024. "Sequential monitoring of stock market price changes," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 156-172.
    4. Gabriela Ciuperca & Matúš Maciak & Michal Pešta, 2024. "Real-time changepoint detection in a nonlinear expectile model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(2), pages 105-131, February.
    5. Farah Mugrabi, 2026. "Financial Contagion: Detecting Non‐Simultaneous Breaks in DCC‐GARCH Models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 2638-2663, April.
    6. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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