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Knowledge Discovery to Support WTI Crude Oil Price Risk Management

Author

Listed:
  • Radosław Puka

    (Faculty of Management, AGH University of Krakow, 30-059 Kraków, Poland)

  • Bartosz Łamasz

    (Faculty of Management, AGH University of Krakow, 30-059 Kraków, Poland)

  • Iwona Skalna

    (Faculty of Management, AGH University of Krakow, 30-059 Kraków, Poland)

  • Beata Basiura

    (Faculty of Management, AGH University of Krakow, 30-059 Kraków, Poland)

  • Jerzy Duda

    (Faculty of Management, AGH University of Krakow, 30-059 Kraków, Poland)

Abstract

The high volatility of commodity prices and various problems that the energy sector has to deal with in the era of COVID-19 have significantly increased the risk of oil price changes. These changes are of the main concern of companies for which oil is the main input in the production process, and therefore oil price determines the production costs. The main goal of this paper is to discover decision rules for a buyer of American WTI (West Texas Intermediate) crude oil call options. The presented research uses factors characterizing the option price, such as implied volatility and option sensitivity factors (delta, gamma, vega, and theta, known as “Greeks”). The performed analysis covers the years 2008–2022 and options with an exercise period up to three months. The decision rules are discovered using association analysis and are evaluated in terms of the three investment efficiency indicators: total payoff, average payoff, and return on investment. The results show the existence of certain ranges of the analyzed parameters for which the mentioned efficiency indicators reached particularly high values. The relationships discovered and recorded in the form of decision rules can be effectively used or adapted by practitioners to support their decisions in oil price risk management.

Suggested Citation

  • Radosław Puka & Bartosz Łamasz & Iwona Skalna & Beata Basiura & Jerzy Duda, 2023. "Knowledge Discovery to Support WTI Crude Oil Price Risk Management," Energies, MDPI, vol. 16(8), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3486-:d:1125089
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    References listed on IDEAS

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    1. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    2. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. "Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    3. Soini, Vesa & Lorentzen, Sindre, 2019. "Option prices and implied volatility in the crude oil market," Energy Economics, Elsevier, vol. 83(C), pages 515-539.
    4. Seungho Baek & Kwan Yong Lee, 2021. "The risk transmission of COVID-19 in the US stock market," Applied Economics, Taylor & Francis Journals, vol. 53(17), pages 1976-1990, April.
    5. Costas Siriopoulos & Athanasios Fassas, 2013. "Dynamic relations of uncertainty expectations: a conditional assessment of implied volatility indices," Review of Derivatives Research, Springer, vol. 16(3), pages 233-266, October.
    6. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
    7. Souza, Rodrigo da Silva & Fry-McKibbin, Renée, 2021. "Global liquidity and commodity market interactions: Macroeconomic effects on a commodity exporting emerging market," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 781-800.
    8. Andreou, Panayiotis C. & Charalambous, Chris & Martzoukos, Spiros H., 2008. "Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1415-1433, March.
    9. Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
    10. Hibbert, Ann Marie & Daigler, Robert T. & Dupoyet, Brice, 2008. "A behavioral explanation for the negative asymmetric return-volatility relation," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2254-2266, October.
    11. Bartosz Łamasz & Natalia Iwaszczuk, 2020. "The Impact of Implied Volatility Fluctuations on Vertical Spread Option Strategies: The Case of WTI Crude Oil Market," Energies, MDPI, vol. 13(20), pages 1-23, October.
    12. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite," Energies, MDPI, vol. 14(24), pages 1-24, December.
    13. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    14. Alqahtani, Abdullah & Selmi, Refk & Hongbing, Ouyang, 2021. "The financial impacts of jump processes in the crude oil price: Evidence from G20 countries in the pre- and post-COVID-19," Resources Policy, Elsevier, vol. 72(C).
    15. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
    16. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    17. Maghyereh, Aktham I. & Awartani, Basel & Bouri, Elie, 2016. "The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 57(C), pages 78-93.
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