Artikel
A hybrid Bayesian-network proposition for forecasting the crude oil price
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Financial Innovation ; ISSN: 2199-4730 ; Volume: 5 ; Year: 2019 ; Issue: 1 ; Pages: 1-21 ; Heidelberg: Springer
- Klassifikation
-
Management
- Thema
-
Bayesian networks
Random Forest
Markov chain Monte Carlo
Support vector machine
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Fazelabdolabadi, Babak
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer
- (wo)
-
Heidelberg
- (wann)
-
2019
- DOI
-
doi:10.1186/s40854-019-0144-2
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Fazelabdolabadi, Babak
- Springer
Entstanden
- 2019
Ähnliche Objekte (12)
A hybrid Bayesian-network proposition for forecasting the crude oil price
Volatility forecasting for crude oil futures
Forecasting Crude Oil Prices Using Reservoir Computing Models
The forecasting power of EPU for crude oil return volatility
Forecasting the price of crude oil via convenience yield predictions
Forecasting the price of crude oil via convenience yield predictions
A blending ensemble learning model for crude oil price forecasting
Modelling and forecasting crude oil price volatility with climate policy uncertainty
Crowd oil not crude oil
Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning
Forecasting the term structure of crude oil futures prices with neural networks
Crude oil prices and the euro-dollar exchange rate: A forecasting exercise
A hybrid Bayesian-network proposition for forecasting the crude oil price
Volatility forecasting for crude oil futures
Forecasting Crude Oil Prices Using Reservoir Computing Models
The forecasting power of EPU for crude oil return volatility
Forecasting the price of crude oil via convenience yield predictions
Forecasting the price of crude oil via convenience yield predictions
A blending ensemble learning model for crude oil price forecasting
Modelling and forecasting crude oil price volatility with climate policy uncertainty
Crowd oil not crude oil
Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning
Forecasting the term structure of crude oil futures prices with neural networks
Crude oil prices and the euro-dollar exchange rate: A forecasting exercise
A hybrid Bayesian-network proposition for forecasting the crude oil price
Volatility forecasting for crude oil futures
Forecasting Crude Oil Prices Using Reservoir Computing Models
The forecasting power of EPU for crude oil return volatility
Forecasting the price of crude oil via convenience yield predictions
Forecasting the price of crude oil via convenience yield predictions
A blending ensemble learning model for crude oil price forecasting
Modelling and forecasting crude oil price volatility with climate policy uncertainty
Crowd oil not crude oil
Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning
Forecasting the term structure of crude oil futures prices with neural networks