Abstract
This paper proposes parametric two-step procedures for assessing the stability of cross-sectional dependency measures in the presence of potential breaks in the marginal distributions. The procedures are based on formerly proposed sup-LR tests in which restricted and unrestricted likelihood functions are compared with each other. First, we show theoretically that standard asymptotics do not hold in this situation. We propose a suitable bootstrap scheme and derive test statistics in different commonly used settings. The properties of the test statistics and precision of the associated change-point estimator are analysed and compared with existing non-parametric methods in various Monte Carlo simulations. These studies reveal advantages in test power for higher-dimensional data and an almost uniform superiority of the sup-LR test in terms of precision of the change-point estimator. We then apply this method to equity returns of European banks during the financial crisis of 2008.
Acknowledgments
We are grateful to a referee, Anne-Florence Allard, Julien Chevallier, Hans Manner and participants at the 2018 Paris Financial Management Conference, 2019 Quantitative Finance Workshop (ETH Zurich), 2019 Econometric Society European Meeting (University of Manchester) for useful comments and suggestions.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2019-0043).
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