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Does the choice of estimator matter when forecasting returns?

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posted on 2012-01-01, 00:00 authored by Joakim WesterlundJoakim Westerlund, Paresh Narayan
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.

History

Series

School Working Paper - Financial Econometrics Series ; SWP 2012/01

Pagination

1 - 29

Publisher

Deakin University, School of Accounting, Economics and Finance

Place of publication

Geelong, Vic.

Language

eng

Notes

School working paper (Deakin University. School of Accounting, Economics and Finance) ; 2012/01 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.

Publication classification

CN.1 Other journal article

Copyright notice

2012, The Authors

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