Testing against changing correlation

Harvey, Andrew & Thiele, Stephen (2016) Testing against changing correlation. Journal of Empirical Finance, Volume 38, pp. 575-589.

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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.

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ID Code: 95167
Item Type: Journal Article
Refereed: Yes
Keywords: Dynamic conditional score, EGARCH, Lagrange multiplier test, Portmanteau test, Time-varying covariance matrices
DOI: 10.1016/j.jempfin.2015.09.003
ISSN: 0927-5398
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Deposited On: 27 Apr 2016 01:36
Last Modified: 13 Jul 2017 01:50

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