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Generalized Spectral Tests for Multivariate Martingale Difference Hypotheses

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  • Xuexin Wang

Abstract

This study proposes new generalized spectral tests for multivariate martingale difference hypotheses, specifically geared toward high-dimensionality scenarios where the dimension of the time series is comparable or even larger than the sample size in practice. We develop an asymptotic theory and a valid wild bootstrapping procedure for the new test statistics, in which the dimension of the time series is fixed. We demonstrate that a bias-reduced version of the test statistics effectively addresses the high-dimensionality concerns. Comprehensive Monte Carlo simulations reveal that the bias-reduced statistic performs substantially better than its competitors. The application to testing the efficient market hypothesis on the U.S. stock market illustrates the usefulness of our proposal.

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  • Xuexin Wang, 2024. "Generalized Spectral Tests for Multivariate Martingale Difference Hypotheses," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1195-1209, October.
    • Handle: RePEc:taf:jnlbes:v:42:y:2024:i:4:p:1195-1209
    DOI: 10.1080/07350015.2024.2301954
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