scholarly journals Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks

2015 ◽  
Author(s):  
Jozef Barunik ◽  
Barbora Malinskk
Keyword(s):  
Neural Networks ◽  
Crude Oil ◽  
Term Structure ◽  
Futures Prices ◽  
Oil Futures ◽  
Crude Oil Futures

scholarly journals Applying Neural Networks to Prices Prediction of Crude Oil Futures

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
John Wei-Shan Hu ◽  
Yi-Chung Hu ◽  
Ricky Ray-Wen Lin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Crude Oil ◽  
Global Economy ◽  
Predictive Power ◽  
Learning Performance ◽  
Futures Prices ◽  
Oil Futures ◽  
Training Time ◽  
Crude Oil Futures

The global economy experienced turbulent uneasiness for the past five years owing to large increases in oil prices and terrorist’s attacks. While accurate prediction of oil price is important but extremely difficult, this study attempts to accurately forecast prices of crude oil futures by adopting three popular neural networks methods including the multilayer perceptron, the Elman recurrent neural network (ERNN), and recurrent fuzzy neural network (RFNN). Experimental results indicate that the use of neural networks to forecast the crude oil futures prices is appropriate and consistent learning is achieved by employing different training times. Our results further demonstrate that, in most situations, learning performance can be improved by increasing the training time. Moreover, the RFNN has the best predictive power and the MLP has the worst one among the three underlying neural networks. This finding shows that, under ERNNs and RFNNs, the predictive power improves when increasing the training time. The exceptional case involved BPNs, suggesting that the predictive power improves when reducing the training time. To sum up, we conclude that the RFNN outperformed the other two neural networks in forecasting crude oil futures prices.

scholarly journals Risk premia in crude oil futures prices

2014 ◽  
Vol 42 ◽  
pp. 9-37 ◽  
Author(s):  
James D. Hamilton ◽  
Jing Cynthia Wu
Keyword(s):  
Crude Oil ◽  
Risk Premia ◽  
Futures Prices ◽  
Oil Futures ◽  
Crude Oil Futures

scholarly journals Risk Premia in Crude Oil Futures Prices

2013 ◽  
Author(s):  
James Hamilton ◽  
Jing Cynthia Wu
Keyword(s):  
Crude Oil ◽  
Risk Premia ◽  
Futures Prices ◽  
Oil Futures ◽  
Crude Oil Futures

scholarly journals Research on the Correlation Between WTI Crude Oil Futures Price and European Carbon Futures Price

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhengwei Ma ◽  
Yuxin Yan ◽  
Ruotong Wu ◽  
Feixiao Li
Keyword(s):  
Crude Oil ◽  
Carbon Emission ◽  
Mutual Effect ◽  
Futures Prices ◽  
Price Fluctuation ◽  
Oil Futures ◽  
Sea Levels ◽  
Crude Oil Futures ◽  
Futures Price ◽  
Emission Rights

In recent years, the rapid increase in CO2 concentration has accelerated global warming. As a result, sea levels rise, glaciers melt, extreme weather occurs, and species become extinct. As the world’s largest CO2 emission rights trading market, EU Emissions Trading System (EU-ETS) has reached 1.855 billion tons of quotas by 2019, influencing the development of the global carbon emission market. Crude oil, as one of the major fossil energy sources in the world, its price fluctuation is bound to affect the price of carbon emission rights. Therefore, this paper aims to reveal the correlation between crude oil futures prices and carbon emission rights futures prices by studying the price fluctuation. In this paper, the linkage between West Texas Intermediate (WTI) crude oil futures prices and European carbon futures prices was investigated. In addition, this paper selects continuous data of WTI crude oil futures prices and spot prices with European carbon futures prices from January 8, 2018 to November 27, 2020, and builds a smooth transformation regression (STR) model. The relationship between crude oil futures and carbon futures prices is studied in both forward and reversal linkage through empirical analysis. The results show that crude oil futures prices and carbon futures prices have a mutual effect on each other, and both linear and nonlinear correlations between the two prices exist. Based on the results of this research, some suggestions are provided.

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