Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk
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 artic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/11/3308 |
| _version_ | 1827690591460261888 |
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| author | Radosław Puka Bartosz Łamasz Marek Michalski |
| author_facet | Radosław Puka Bartosz Łamasz Marek Michalski |
| author_sort | Radosław Puka |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:42:17Z |
| format | Article |
| id | doaj.art-b8a08f2387204008ba1bd5b941688919 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T10:42:17Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b8a08f2387204008ba1bd5b9416889192023-11-21T22:51:44ZengMDPI AGEnergies1996-10732021-06-011411330810.3390/en14113308Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price RiskRadosław Puka0Bartosz Łamasz1Marek Michalski2Faculty of Management, AGH University of Science and Technology, 30-059 Cracow, PolandFaculty of Management, AGH University of Science and Technology, 30-059 Cracow, PolandFaculty of Management, AGH University of Science and Technology, 30-059 Cracow, PolandDespite 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.https://www.mdpi.com/1996-1073/14/11/3308effectiveness analysiscrude oil price riskcommodity optionsartificial neural networks (ANNs)support decision-making |
| spellingShingle | Radosław Puka Bartosz Łamasz Marek Michalski Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk Energies effectiveness analysis crude oil price risk commodity options artificial neural networks (ANNs) support decision-making |
| title | Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk |
| title_full | Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk |
| title_fullStr | Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk |
| title_full_unstemmed | Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk |
| title_short | Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk |
| title_sort | effectiveness of artificial neural networks in hedging against wti crude oil price risk |
| topic | effectiveness analysis crude oil price risk commodity options artificial neural networks (ANNs) support decision-making |
| url | https://www.mdpi.com/1996-1073/14/11/3308 |
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