Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?

Li-Min Xing and Yue-Jun Zhang ()

Energy Economics, 2022, vol. 110, issue C

Abstract: This study explores different specifications of shrinkage methods to forecast crude oil prices, and examines the forecasting performances from both statistical and economic perspectives. In addition to the widely-used LASSO, elastic net and ridge, we consider two popular nonvonvex penalties and Huber loss function in penalized regressions. The results indicate that, first, the out-of-sample performance of different shrinkage methods depend on the forecasting horizons. The shrinkage forecasts with nonvonvex penalty and Huber loss outperform the benchmark and competing models within one year; the popular shrinkage methods of LASSO, elastic net and ridge perform relatively well more than one year, whereas, no better than the no-change benchmark. Second, in terms of net-of-transaction-costs portfolio performance for different horizon forecasts, the portfolios based on nonconvex penalties achieve the majority of the largest economic gains, followed by the popular version of LASSO and elastic net. When crude oil prices decline sharply, shrinkage forecasts outperform the benchmark forecast remarkably, both in statistical and economic perspectives. Finally, extended analysis indicates that imposing statistical and economic constraints on coefficient estimation of shrinkage models can further improve forecasting performance in most cases.

Keywords: Crude oil price; Regularized constraint; Out-of-sample forecasting; Economic gain; Transaction cost (search for similar items in EconPapers)
JEL-codes: C53 E37 F37 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988322001852
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:110:y:2022:i:c:s0140988322001852

DOI: 10.1016/j.eneco.2022.106014

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().