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Detrending Persistent Predictors



Researchers in finance very often rely on highly persistent Ñ nearly integrated Ñ explanatory variables to predict returns. This paper proposes to stand up to the usual problem of persistent regressor bias, by detrending the highly auto-correlated predictors. We find that the statistical evidence of out-of-sample predictability of stock returns is stronger, once predictors are adjusted for high persistence

Suggested Citation

  • Christophe Boucher & Bertrand Maillet, 2011. "Detrending Persistent Predictors," Documents de travail du Centre d'Economie de la Sorbonne 11019, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  • Handle: RePEc:mse:cesdoc:11019

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    References listed on IDEAS

    1. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
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    Forecasting; persistence; detrending; expected returns;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G1 - Financial Economics - - General Financial Markets

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