Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

Krzysztof Drachal

Energies, 2022, vol. 16, issue 1, 1-29

Abstract: In this study, the crude oil spot price is forecast using Bayesian symbolic regression (BSR). In particular, the initial parameters specification of BSR is analysed. Contrary to the conventional approach to symbolic regression, which is based on genetic programming methods, BSR applies Bayesian algorithms to evolve the set of expressions (functions). This econometric method is able to deal with variable uncertainty (feature selection) issues in oil price forecasting. Secondly, this research seems to be the first application of BSR to oil price forecasting. Monthly data between January 1986 and April 2021 are analysed. As well as BSR, several other methods (also able to deal with variable uncertainty) are used as benchmark models, such as LASSO and ridge regressions, dynamic model averaging, and Bayesian model averaging. The more common ARIMA and naïve methods are also used, together with several time-varying parameter regressions. As a result, this research not only presents a novel and original application of the BSR method but also provides a concise and uniform comparison of the application of several popular forecasting methods for the crude oil spot price. Robustness checks are also performed to strengthen the obtained conclusions. It is found that the suitable selection of functions and operators for BSR initialization is an important, but not trivial, task. Unfortunately, BSR does not result in forecasts that are statistically significantly more accurate than the benchmark models. However, BSR is computationally faster than the genetic programming-based symbolic regression.

Keywords: Bayesian symbolic regression; crude oil; model uncertainty; oil price; symbolic regression; time series forecasting; variable uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/1/4/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/1/4/ (text/html)

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:gam:jeners:v:16:y:2022:i:1:p:4-:d:1008576

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().