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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," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(6), pages 1993-2001, September.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:6:p:1993-2001
    DOI: 10.1002/for.3280
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