Forecasting crude oil prices: A scaled PCA approach

Mengxi He, Yaojie Zhang, Danyan Wen and Yudong Wang

Energy Economics, 2021, vol. 97, issue C

Abstract: In this paper, we employ a novel dimension reduction approach, the scaled principal component analysis (s-PCA), to improve the oil price predictability with technical indicators. The empirical results show that the s-PCA model outperforms various competing models both in- and out-of-sample. From a market timing perspective, an oil futures investor can realize a larger Sharpe ratio using the s-PCA approach than using the competing models and Buy-and-Hold strategy. Furthermore, we investigate the driving forces behind the superior performance of the s-PCA model from a loading perspective. We illustrate that the s-PCA model can identify technical indicators with strong predictive power and put relatively large loadings on them when constructing diffusion indexes. Finally, our results are robust to a series of settings.

Keywords: Oil price predictability; Technical indicators; PCA; Supervised learning; Market timing (search for similar items in EconPapers)
JEL-codes: C32 C53 G17 Q47 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (59)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988321000943
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:97:y:2021:i:c:s0140988321000943

DOI: 10.1016/j.eneco.2021.105189

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 ().