Small-sample tests for stock return predictability with possibly non-stationary regressors and GARCH-type effects

被引：4

作者：

Gungor, Sermin ^{[1
]}

Luger, Richard ^{[2
]}

机构：

[1] Bank Canada, Financial Markets Dept, Ottawa, ON K1A 0G9, Canada

[2] Laval Univ, Dept Finance Insurance & Real Estate, Quebec City, PQ G1V 0A6, Canada

来源：

JOURNAL OF ECONOMETRICS | 2020年 / 218卷 / 02期

关键词：

Stock returns; Predictive regression; Multiple predictors; Unit roots; Conditional heteroskedasticity; Robust inference; EXPECTED RETURNS; ASSET RETURNS; RANDOM-WALK; INFERENCE; HYPOTHESIS; PREDICTOR; LEVERAGE;

D O I：

10.1016/j.jeconom.2020.04.037

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We develop a simulation-based procedure to test for stock return predictability with multiple regressors. The process governing the regressors is left completely free and the test procedure remains valid in small samples even in the presence of non-normalities and GARCH-type effects in the stock returns. The usefulness of the new procedure is demonstrated in a simulation study and by examining the ability of a group of financial variables to predict excess stock returns. We find some evidence of predictability during the period 1948-2014, driven entirely by the term spread. This empirical evidence, however, is much weaker over subsamples. (C) 2020 The Author(s). Published by Elsevier B.V.

引用

页码：750 / 770

页数：21