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...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-ofsample 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.展开更多
This study investigates whether the implied crude oil volatility and the historical OPEC price volatility can impact the return to and volatility of the energy-sector equity indices in Iran.The analysis specifically c...This study investigates whether the implied crude oil volatility and the historical OPEC price volatility can impact the return to and volatility of the energy-sector equity indices in Iran.The analysis specifically considers the refining,drilling,and petrochemical equity sectors of the Tehran Stock Exchange.The parameter estimation uses the quasi-Monte Carlo and Bayesian optimization methods in the framework of a generalized autoregressive conditional heteroskedasticity model,and a complementary Bayesian network analysis is also conducted.The analysis takes into account geopolitical risk and economic policy uncertainty data as other proxies for uncertainty.This study also aims to detect different price regimes for each equity index in a novel way using homogeneous/non-homogeneous Markov switching autoregressive models.Although these methods provide improvements by restricting the analysis to a specific price-regime period,they produce conflicting results,rendering it impossible to draw general conclusions regarding the contagion effect on returns or the volatility transmission between markets.Nevertheless,the results indicate that the OPEC(historical)price volatility has a stronger effect on the energy sectors than the implied volatility has.These types of oil price shocks are found to have no effect on the drilling sector price pattern,whereas the refining and petrochemical equity sectors do seem to undergo changes in their price patterns nearly concurrently with future demand shocks and oil supply shocks,respectively,gaining dominance in the oil market.展开更多
The application of a recent optimization technique,the artificial bee colony(ABC),was investigated in the context of finding the optimal well locations.The ABC performance was compared with the corresponding results f...The application of a recent optimization technique,the artificial bee colony(ABC),was investigated in the context of finding the optimal well locations.The ABC performance was compared with the corresponding results from the particle swarm optimization(PSO)algorithm,under essentially similar conditions.Treatment of out-of-boundary solution vectors was accomplished via the Periodic boundary condition(PBC),which presumably accelerates convergence towards the global optimum.Stochastic searches were initiated from several random staring points,to minimize starting-point dependency in the established results.The optimizations were aimed at maximizing the Net Present Value(NPV)objective function over the considered oilfield production durations.To deal with the issue of reservoir heterogeneity,random permeability was applied via normal/uniform distribution functions.In addition,the issue of increased number of optimization parameters was address,by considering scenarios with multiple injector and producer wells,and cases with deviated wells in a real reservoir model.The typical results prove ABC to excel PSO(in the cases studied)after relatively short optimization cycles,indicating the great premise of ABC methodology to be used for well-optimization purposes.展开更多
文摘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-ofsample 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.
文摘This study investigates whether the implied crude oil volatility and the historical OPEC price volatility can impact the return to and volatility of the energy-sector equity indices in Iran.The analysis specifically considers the refining,drilling,and petrochemical equity sectors of the Tehran Stock Exchange.The parameter estimation uses the quasi-Monte Carlo and Bayesian optimization methods in the framework of a generalized autoregressive conditional heteroskedasticity model,and a complementary Bayesian network analysis is also conducted.The analysis takes into account geopolitical risk and economic policy uncertainty data as other proxies for uncertainty.This study also aims to detect different price regimes for each equity index in a novel way using homogeneous/non-homogeneous Markov switching autoregressive models.Although these methods provide improvements by restricting the analysis to a specific price-regime period,they produce conflicting results,rendering it impossible to draw general conclusions regarding the contagion effect on returns or the volatility transmission between markets.Nevertheless,the results indicate that the OPEC(historical)price volatility has a stronger effect on the energy sectors than the implied volatility has.These types of oil price shocks are found to have no effect on the drilling sector price pattern,whereas the refining and petrochemical equity sectors do seem to undergo changes in their price patterns nearly concurrently with future demand shocks and oil supply shocks,respectively,gaining dominance in the oil market.
文摘The application of a recent optimization technique,the artificial bee colony(ABC),was investigated in the context of finding the optimal well locations.The ABC performance was compared with the corresponding results from the particle swarm optimization(PSO)algorithm,under essentially similar conditions.Treatment of out-of-boundary solution vectors was accomplished via the Periodic boundary condition(PBC),which presumably accelerates convergence towards the global optimum.Stochastic searches were initiated from several random staring points,to minimize starting-point dependency in the established results.The optimizations were aimed at maximizing the Net Present Value(NPV)objective function over the considered oilfield production durations.To deal with the issue of reservoir heterogeneity,random permeability was applied via normal/uniform distribution functions.In addition,the issue of increased number of optimization parameters was address,by considering scenarios with multiple injector and producer wells,and cases with deviated wells in a real reservoir model.The typical results prove ABC to excel PSO(in the cases studied)after relatively short optimization cycles,indicating the great premise of ABC methodology to be used for well-optimization purposes.
