On the predictive accuracy of crude oil futures prices

Abosedra, Salah; Baghestani, Hamid

doi:10.1016/s0301-4215(03)00104-6

Cited by 82 publications

(41 citation statements)

References 8 publications

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“…To the extent that F t and P t differ, studies by Bopp and Lady (1991), Abosedraa and Baghestani (2004), Chinn, LeBlanc and Coibion (2005), and Alquist and Kilian (2008) found that P t provides as good or even a better forecast of P t+s than does the futures price F t .…”

Section: Futures Marketsmentioning

confidence: 99%

Understanding Crude Oil Prices

Hamilton

2008

264

208

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This paper examines the factors responsible for changes in crude oil prices. The paper reviews the statistical behavior of oil prices, relates these to the predictions of theory, and looks in detail at key features of petroleum demand and supply. Topics discussed include the role of commodity speculation, OPEC, and resource depletion. The paper concludes that although scarcity rent made a negligible contribution to the price of oil in 1997, it could now begin to play a role.

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Section: Futures Marketsmentioning

confidence: 99%

Understanding Crude Oil Prices

Hamilton

2008

264

208

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“…Bopp and Sitzer (1987) reveals that while futures price can predict cash price via improving the forecasts in econometrical models, only future contract of 1-to 2-months maturity are statistically significant as to contain new data. Also, Chan (1992) and Abosedra and Baghestani (2004) concurred that the cash market is a firm source of new data and that futures price 1-to 12-months ahead yields significant forecast and useful for policy making. Moshiri and Foroutan (2005) examined the chaotic and nonlinear feat in futures prices, comparing ARMA/GARCH linear models against the nonlinear ANN model.…”

Section: Introductionmentioning

confidence: 94%

Forecasting Price Direction, Hedging and Spread Options in Oil Volatility

Okonji¹

2017

IJEBO

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Abstract:The energy market aims to manage risks associated with prices and volatility of the asset. It is a capital intensive market, rippled with a range of chaotic, complex and dynamic interaction among its supply and demand derivatives. Models help users forecast such interactions, to provide investors with empirical evidence of the price direction. Evolutionary modeling is an art, whose science seeks to analyze input data and yield an optimal, complete solution for which conventional methods yield a corresponding, non-cost effective solution. Its solutions are tractable, robust and low-cost with a tolerance of ambiguity, uncertainty and noise as applied to its input. Our study aims to predict the OPEC Oil market with data collected over the period.

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“…Using prices determined in the futures oil market has been suggested as a forecasting methodology [22][23][24]. Such an approach tests if the futures price is an unbiased predictor of the spot price at the maturity time.…”

Section: Introductionmentioning

confidence: 99%

“…Yoon [22] used WTI spot and futures prices from July 2000 to June 2004 as sample data, selecting the forecasting period that yielded the most accurate forecasts by comparing quarterly forecasts based on futures prices from the previous one to six months with the average of the quarterly WTI oil prices. Similarly, Abosedra and Baghestani [23] evaluated forecasting accuracy by comparing futures prices (1,3,6,9, and 12 months out) with WTI spot prices from January 1991 to December 2001. Yanagisawa [24] analyzed if futures prices from a certain time could be appropriately used to forecast spot prices by testing the Granger causality between WTI spot prices and futures prices.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

Lee

Huh

2017

Sustainability

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Abstract:In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model's forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040.

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On the predictive accuracy of crude oil futures prices

Cited by 82 publications

References 8 publications

Understanding Crude Oil Prices

Understanding Crude Oil Prices

Forecasting Price Direction, Hedging and Spread Options in Oil Volatility

Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

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