Predicting daily oil prices: Linear and non-linear models

Dbouk, Wassim; Jamali, Ibrahim

doi:10.1016/j.ribaf.2018.01.003

Cited by 24 publications

(14 citation statements)

References 73 publications

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“…Here, since the 2000s, genetic algorithms have been used in order to improve the characteristics of other methods (Fan et al 2008;Amin-Naseri and Gharacheh 2007). Especially popular were combinations with artificial neural networks and their various combinations (Chiroma et al 2015;Chiroma et al 2014;Chiroma and Abdulkareem 2016;Dbouk and Jamali 2018;Mirmirani and Li 2004); in fact, they are still being proposed (Herawati and Djunaidy 2020). Between 2006 and 2017, there was a growing interest in applying GAs to methods such as support vector machines or leastsquares support vector regression (Guo et al 2012;Huang and Wang 2006;Yu et al 2016;Li and Ge 2013;Čeperić et al 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The assumed null hypothesis is that the predicted WTI prices and the ones observed are the same (Chiroma et al 2015). Genetic algorithms can be also used in oil price predictions to optimize other neural network parameters, such as bias, learning rate, momentum, and the number of hidden neurons (Chiroma et al 2014;Chiroma and Abdulkareem 2016), as well as training a neural network (Dbouk and Jamali 2018). Herawati and Djunaidy (2020) used it to optimize the parameters of a feedforward neural network in order to avoid overfitting and find local optima as final solutions.…”

Section: Energy Commoditiesmentioning

confidence: 99%

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A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

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Section: Discussionmentioning

confidence: 99%

Section: Energy Commoditiesmentioning

confidence: 99%

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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“…These and other ANNs models [47][48][49][50][51][52][53][54][55][56] have shown that ANNs provide an important alternative to econometrics (both linear and nonlinear) in forecasting crude oil prices. Dbouk et al [57] noted, however, that the accuracy of price predictions is not a key aspect of successful investment or hedging strategies. It is also worth noting that the main aim of our research was not to use the ANNs to predict future oil prices, but to search for call options purchase signals with the use of this tool.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

2021

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Despite the growing share of renewable energy sources, most of the world energy supply is still based on hydrocarbons and the vast majority of world transport is fuelled by oil products. Thus, the profitability of many companies may depend on the effective management of oil price risk. In this article, we analysed the effectiveness of artificial neural networks in hedging against the risk of WTI crude oil prices increase. This was reformulated from a regressive problem to a classification problem. The effectiveness of our approach, using artificial neural networks to classify observations, was verified for over ten years of WTI futures quotes, starting from 2009. The data analysis presented in this paper confirmed that the buyer of a call option was more often likely to incur a loss as a result of its purchase than make a profit after the final payoff from the call option. The results of the conducted research confirm that neural networks can be an effective form of protection against the risk of price fluctuations. The effectiveness of a network’s operation depends on the choice of assessment indicators, but analyses show that the networks which, for the indicator that was selected, gave the best results for the training set, also resulted in positive rates of return for the test set. Significantly, we also showed interdependence between seemingly unrelated indicators: percentage of the best possible results achieved in the analysed period of time by the proposed method and percentage of all available call options that were purchased based on the results from the networks that were used.

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“…We relied on the non-linear autoregressive distributed lag (NARDL) estimation procedure of Shin, Yu, and Greenwood-Nimmo (2014)to reach policy consistent outcomes on the fossil-fuel subsidy-carbon emission relations in Nigeria. Over time, research has established nonlinearity in oil prices (see Khraief, Shahbaz, Mahalik, & Bhattacharya (2021); Xu, Han, Wan, & Yin (2019); Dbouk & Jamali (2018) for some examples), making the adoption of conventional linear estimation procedures indeterminable and inappropriate to reach a plausible outcome on the subject. Unlike the ARDL bound cointegration procedure followed in Dey, and Tareque (2019), the NARDL estimation technique is useful in establishing both short-run and long-run asymmetric co-integrating characteristics the variables.…”

Section: Introductionmentioning

confidence: 99%

Fuel subsidies and carbon emission: evidence from asymmetric modelling

Adekunle

Oseni

2021

Environ Sci Pollut Res

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It is expected that fuel subsidy removal should hinder carbon emissions growth through low energy consumption channels amid higher energy prices. However, outliers in this theoretical disposition make empirical proof of the fuel subsidy-carbon intensity apt and primitive. Despite established fuel subsidy abolishment gains for climate and economic welfare, the relevance, magnitude and policy implications remain dimly. This paper employs the non-linear autoregressive distributed lag (NARDL) estimation procedure to gauge the contemporaneous influence of fuel subsidy for carbon intensity in Nigeria. Findings revealed that fuel subsidy removal inversely relates to Nigeria's carbon emission in the short-run and long run. The study recommends complementary policy option that ensures additional financial savings to the government should be invested in public sector growth that can cushion the effect of relative income loss to the citizenry. The Nigerian government should ensure measures are kept in place to discourage over-consumption of alternative energy (for example, coal) that could also threaten the green economy paradox.

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Predicting daily oil prices: Linear and non-linear models

Cited by 24 publications

References 73 publications

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

Fuel subsidies and carbon emission: evidence from asymmetric modelling

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