Is hedging the crack spread no longer all it's cracked up to be?

Liu, Pan; Vedenov, Dmitry V.; Power, Gabriel J.

doi:10.1016/j.eneco.2017.01.020

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…Therefore, the oil price dynamics at these ports may contain predictive information for the changes in the price of Brent oil. The oil crack spread, which measures the oil refining margin. More specifically, the oil crack spread is the difference in the prices between crude oil and its refined products and is computed as a fixed 3:2:1 ratio between the futures positions of crude oil, gasoline, and heating oil (Liu et al, 2017). The oil crack spread is employed as a predictor in light of the findings in the literature which lend support to it being a useful predictor of oil prices (Murat & Tokat, 2009) or a complement to oil forecasting models (Baumeister et al, 2018).…”

Section: Data and Predictorsmentioning

confidence: 99%

A forecasting model for oil prices using a large set of economic indicators

El Hokayem,

Jamali,

Hejase

2024

Journal of Forecasting

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This paper examines the predictability of the changes in Brent oil futures prices using a multilayer perceptron artificial neural network that exploits the information contained in the largest possible set of economic indicators. Feature engineering is employed to identify the most important predictors of the change in Brent oil futures prices. We find that oil‐market‐specific variables are important predictors. Our findings also suggest that forecasts of the change in the Brent oil futures prices from the multilayer perceptron that exploits the informational content of all and oil‐market‐specific predictors exhibit higher statistical forecast accuracy than the random walk. Tests of forecast optimality indicate that the forecasts generated using oil‐market‐specific predictors are optimal. We discuss the policymaking and practical relevance of our results.

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Section: Data and Predictorsmentioning

confidence: 99%

A forecasting model for oil prices using a large set of economic indicators

El Hokayem,

Jamali,

Hejase

2024

Journal of Forecasting

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“…Billio et al [14] introduced the concept of portfolio into hedging and held that futures market is an investment market, and the purpose of hedgers entering futures market is to obtain the highest returns under certain risks. Liu et al [15] put forward the optimal HR, and since then, the determination of HR and the measurement of hedging performance have aroused the upsurge of theoretical and empirical research. On the premise of maximizing the utility function, Bodnar et al [16] gave the calculation method of HR, and the result is the HR with maximized utility.…”

Section: Related Workmentioning

confidence: 99%

A Minimum Variance Hedging Ratio Model Based on Nonlinear Grey Classification Model

Xia¹,

Zhao²

2022

Wireless Communications and Mobile Computing

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The risk transfer function of futures market is mainly realized by hedging strategy. Futures price yield and spot price yield tend to show different fluctuations before and during hedging, which leads to the distortion of hedging ratio, that is, the calculated hedging effect is weaker than the traditional hedging effect. On the basis of MV (minimum variance) hedging model, this paper introduces NGCM (nonlinear grey classification model) to solve the nonlinear correlation between futures and spot returns, which can improve the hedging effect. The results show that, due to the existence of basis, the price change model violates the linear assumption of OLS (ordinary least squares) parameters, and there is a problem of model missetting. It is estimated that HR (hedging ratio) should choose the price model, which can better depict the linkage between futures price and spot price. The effectiveness of HR in this study is higher than that of existing models. Applying this model to hedge can effectively avoid the spot price risk. Investors can reasonably choose the hedging model according to their own needs.

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Hedging downside risk of oil refineries: A vine copula approach

Sukcharoen

Leatham

2017

Energy Economics

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Is hedging the crack spread no longer all it's cracked up to be?

Cited by 11 publications

References 21 publications

A forecasting model for oil prices using a large set of economic indicators

A forecasting model for oil prices using a large set of economic indicators

A Minimum Variance Hedging Ratio Model Based on Nonlinear Grey Classification Model

Hedging downside risk of oil refineries: A vine copula approach

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