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Testing for Linear and Nonlinear Predictability of Stock Returns

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  • Markku Lanne
  • Mika Meitz
  • Pentti Saikkonen

Abstract

We develop tests for predictability in a first-order ARMA model often suggested for stock returns. Instead of the conventional ARMA model, we consider its non-Gaussian and noninvertible counterpart that has identical autocorrelation properties but allows for conditional heteroskedasticity prevalent in stock returns. In addition to autocorrelation, the tests can also be used to test for nonlinear predictability, in contrast to previously proposed predictability tests based on invertible ARMA models. Simulation results attest to improved power. We apply our tests to postwar U.S. stock returns. All return series considered are found serially uncorrelated but dependent and, hence, nonlinearly predictable. Copyright The Author, 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.

Suggested Citation

  • Markku Lanne & Mika Meitz & Pentti Saikkonen, 2013. "Testing for Linear and Nonlinear Predictability of Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 11(4), pages 682-705, September.
    • Handle: RePEc:oup:jfinec:v:11:y:2013:i:4:p:682-705
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbt004
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    Cited by:

    1. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2017. "Testing for fundamental vector moving average representations," Quantitative Economics, Econometric Society, vol. 8(1), pages 149-180, March.
    2. Weifeng Jin, 2023. "Quantile Autoregression-based Non-causality Testing," Papers 2301.02937, arXiv.org.
    3. Alain Hecq & Daniel Velasquez-Gaviria, 2023. "Spectral identification and estimation of mixed causal-noncausal invertible-noninvertible models," Papers 2310.19543, arXiv.org.
    4. Nyholm, Juho, 2017. "Residual-based diagnostic tests for noninvertible ARMA models," MPRA Paper 81033, University Library of Munich, Germany.

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