A hybrid Bayesian-network proposition for forecasting the crude oil price

Artikel

This paper proposes a hybrid Bayesian Network (BN) method for short-term forecasting of crude oil prices. The method performed is a hybrid, based on both the aspects of classification of influencing factors as well as the regression of the out-of-sample values. For the sake of performance comparison, several other hybrid methods have also been devised using the methods of Markov Chain Monte Carlo (MCMC), Random Forest (RF), Support Vector Machine (SVM), neural networks (NNET) and generalized autoregressive conditional heteroskedasticity (GARCH). The hybrid methodology is primarily reliant upon constructing the crude oil price forecast from the summation of its Intrinsic Mode Functions (IMF) and its residue, extracted by an Empirical Mode Decomposition (EMD) of the original crude price signal. The Volatility Index (VIX) as well as the Implied Oil Volatility Index (OVX) has been considered among the influencing parameters of the crude price forecast. The final set of influencing parameters were selected as the whole set of significant contributors detected by the methods of Bayesian Network, Quantile Regression with Lasso penalty (QRL), Bayesian Lasso (BLasso) and the Bayesian Ridge Regression (BRR). The performance of the proposed hybrid-BN method is reported for the three crude price benchmarks: West Texas Intermediate, Brent Crude and the OPEC Reference Basket.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Financial Innovation ; ISSN: 2199-4730 ; Volume: 5 ; Year: 2019 ; Issue: 1 ; Pages: 1-21 ; Heidelberg: Springer

Classification and Topics

• Classification: Management
• Subject: Bayesian networks ; Random Forest ; Markov chain Monte Carlo ; Support vector machine

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Fazelabdolabadi, Babak
• Event: Veröffentlichung
• (who): Springer
• (where): Heidelberg
• (when): 2019

Further information

• DOI: doi:10.1186/s40854-019-0144-2
• Handle: http://hdl.handle.net/10419/237174
• Last update: 10.03.2025, 11:43 AM CET