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A deep learning test of the martingale difference hypothesis

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  • João A. Bastos

Abstract

A deep learning binary classifier is proposed to test if asset returns follow martingale difference sequences. The Neyman-Pearson classification paradigm is applied to control the type I error of the test. In Monte Carlo simulations, I find that this approach has better power properties than variance ratio and portmanteau tests against several alternative processes. I apply this procedure to a large set of exchange rate returns and find that it detects several potential deviations from the martingale difference hypothesis that the conventional statistical tests fail to capture.

Suggested Citation

  • João A. Bastos, 2025. "A deep learning test of the martingale difference hypothesis," Working Papers REM 2025/0374, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp03742025
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    References listed on IDEAS

    as
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    Keywords

    Martingale difference hypothesis; Convolutional network; Variance ratio test; Portmanteau test; Exchange rates.;
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