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

Barassi, M., Horváth, L., Zhao, Y. (2020) Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models. Journal of Business and Economic Statistics, 38 (2). pp. 340-349. ISSN 0735-0015. (doi:10.1080/07350015.2018.1505630) (KAR id:93924)

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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.

Item Type: Article
DOI/Identification number: 10.1080/07350015.2018.1505630
Uncontrolled keywords: Change-point detection; Contagion effect; Monte Carlo simulation; Time varying correlation structure; Volatility processes
Subjects: H Social Sciences
H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Yuqian Zhao
Date Deposited: 17 May 2022 09:59 UTC
Last Modified: 18 May 2022 10:22 UTC
Resource URI: (The current URI for this page, for reference purposes)
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