A deep learning test of the martingale difference hypothesis

João Bastos

No 2025/0374, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa

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.

Keywords: Martingale difference hypothesis; Convolutional network; Variance ratio test; Portmanteau test; Exchange rates. (search for similar items in EconPapers)
Date: 2025-03
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