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Does the choice of estimator matter when forecasting returns?

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  • Westerlund, Joakim
  • Narayan, Paresh Kumar

Abstract

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.

Suggested Citation

  • Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    • Handle: RePEc:eee:jbfina:v:36:y:2012:i:9:p:2632-2640
    DOI: 10.1016/j.jbankfin.2012.06.005
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    More about this item

    Keywords

    Predictive regression; Stock return predictability; Heteroskedasticity; Predictor endogeneity;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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