Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?
By Yaojie Zhang, Feng Ma, Yudong Wang. 2019
262 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/e0028cfdd47beb2b7ab1c8ec380ef08146dde8e8
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In Economics and Econometrics
In Management Science and Operations Research
Citations in the corpus
- Uncertainty and crude oil market volatility: new evidence (2020, methodology): “…two shrinkage methods, namely, least absolute shrinkage and selection operator (Lasso) proposed by Tibshirani (1996) and the elastic net proposed by Zou and Hastie (2005), which are proven to be an effective way to solve overfitting problem by various studies (Li and Chen 2014; Zhang et al. 2019b).”