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Harvey, A. C. and Thiele, S.

Testing against Changing Correlation


Abstract: A test for time-varying correlation is developed within the framework of a dynamic conditional score (DCS) model for both Gaussian and Student t-distributions. The test may be interpreted as a Lagrange multiplier test and modified to allow for the estimation of models for time-varying volatility in the individual series. Unlike standard moment-based tests, the score-based test statistic includes information on the level of correlation under the null hypothesis and local power arguments indicate the benefits of doing so. A simulation study shows that the performance of the score-based test is strong relative to existing tests across a range of data generating processes. An application to the Hong Kong and South Korean equity markets shows that the new test reveals changes in correlation that are not detected by the standard moment-based test.

Keywords: Dynamic conditional score, EGARCH, Lagrange multiplier test, Portmanteau test, Time-varying covariance matrices

JEL Codes: C14 C22 F36

Author links: Andrew Harvey  


Open Access Link:

Published Version of Paper: Testing against changing correlation, Harvey, A. C.and Thiele, S., Journal of Empirical Finance (2016)

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