 Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?Yaojie Zhang, Feng Ma and Yudong Wang Journal of Empirical Finance, 2019, vol. 54, issue C, 97-117 Abstract: In this paper, we use two prevailing shrinkage methods, the lasso and elastic net, to predict oil price returns with a large set of predictors. The out-of-sample results indicate that the lasso and elastic net models outperform a host of widely used competing models in terms of out-of-sample R-square and success ratio. In an asset allocation exercise, a mean–variance investor obtains positive and sizeable economic gains based on the return forecasts of the lasso and elastic net methods relative to both the benchmark forecasts and competing forecasts. We further investigate the source of predictability from a variable selection perspective. The lasso and elastic net methods are found to select powerful predictors and the ones that can provide complementary information. The OLS regression models based on the selected predictors also exhibit better out-of-sample performances than the competing models. In addition, our results are robust to various settings. Keywords: Oil price predictability; Out-of-sample forecasts; Lasso; Elastic net; Variable selection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:54:y:2019:i:c:p:97-117 DOI: 10.1016/j.jempfin.2019.08.007 Access Statistics for this article Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff More articles in Journal of Empirical Finance from Elsevier |
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