IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0309975.html
   My bibliography  Save this article

Modeling the distribution of jet fuel price returns based on fat-tail stable Paretian distribution

Author

Listed:
  • Shuang Lin
  • Shengda Zhang
  • Chaofeng Wang
  • Fan He
  • Zhizhen Xu
  • Yuchen Zhang

Abstract

Jet fuel plays a crucial role as an essential energy source in aerospace and aviation operations. The recent increase in fuel prices has presented airlines with the new challenge of managing jet fuel costs to ensure consistent cash flow and minimize operational uncertainties. The conventional risk prediction models used by airlines often assume that risks are normally distributed according to the classical Central Limit Theorem, which can lead to under-hedging. This paper proposes an innovative approach using the stable Paretian model to analyze the price return of jet fuel in large samples. It comprehensively compares the fitting effect of the stable Paretian distribution with that of the normal distribution based on specific criteria and non-parametric significance tests. Furthermore, it investigates the accuracy of risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) predicted by both models. In addition to comparing differences in VaR between predicted values and actual values, this paper provides a more comprehensive comparison of risk measures under rolling window forecast situation. Results suggest that despite indistinguishable results in VaR backtest, the stable Paretian distribution has a overall better fitting effect as well as a less biased predicted CVaR based on the AIC of -14099.46, BIC of -14110.98, p = 0.58 in Kolmogorov-Smirnov test and p = 0.46(0.92) in the 0.01(0.05) significance level of Expected Shortfall Regression Test. This might be explained by its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines’ risk management decisions. its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines’ risk management decisions.

Suggested Citation

  • Shuang Lin & Shengda Zhang & Chaofeng Wang & Fan He & Zhizhen Xu & Yuchen Zhang, 2024. "Modeling the distribution of jet fuel price returns based on fat-tail stable Paretian distribution," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0309975
    DOI: 10.1371/journal.pone.0309975
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0309975
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0309975&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0309975?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tong Liu & Yanlin Shi, 2022. "Innovation of the Component GARCH Model: Simulation Evidence and Application on the Chinese Stock Market," Mathematics, MDPI, vol. 10(11), pages 1-18, June.
    2. Shuang Lin & Minke Wang & Zhihong Cheng & Fan He & Jiuhao Chen & Chuanhui Liao & Shengda Zhang, 2022. "Risk Management of Fuel Hedging Strategy Based on CVaR and Markov Switching GARCH in Airline Company," Sustainability, MDPI, vol. 14(22), pages 1-9, November.
    3. John Weirstrass Muteba Mwamba & Sutene Mwambetania Mwambi, 2021. "Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula," IJFS, MDPI, vol. 9(2), pages 1-22, May.
    4. Swidan, Hassan & Merkert, Rico, 2019. "The relative effect of operational hedging on airline operating costs," Transport Policy, Elsevier, vol. 80(C), pages 70-77.
    5. Eyden Samunderu, 2023. "Jet Fuel Price Risk and Proxy Hedging in Spot Markets: A Two-Tier Model Analysis," Commodities, MDPI, vol. 2(3), pages 1-32, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Swidan, Hassan & Merkert, Rico & Kwon, Oh Kang, 2019. "Designing optimal jet fuel hedging strategies for airlines – Why hedging will not always reduce risk exposure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 20-36.
    2. Ozan Evkaya & İsmail Gür & Bükre Yıldırım Külekci & Gülden Poyraz, 2024. "Vine Copula Approach to Understand the Financial Dependence of the Istanbul Stock Exchange Index," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2935-2980, November.
    3. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    4. Wang, Ningli & Zhou, Qichong, 2022. "Does commodity hedging with derivatives reduce stock price volatility?," Finance Research Letters, Elsevier, vol. 50(C).
    5. Gongtao Zhang & Huanyu Zhao & Rujie Fan, 2024. "Predicting the volatility of Chinese stock indices based on realized recurrent conditional heteroskedasticity," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-20, October.
    6. Fredy Pokou & Jules Sadefo Kamdem & François Benhmad, 2024. "Empirical Performance of an ESG Assets Portfolio from US Market," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1569-1638, September.
    7. Samunderu, E. & Perret, J.K. & Geller, G., 2023. "The economic value rationale of fuel hedging: An empirical perspective from the global airline industry," Journal of Air Transport Management, Elsevier, vol. 106(C).
    8. Zhang, Wenbei & Luckert, Marty & Qiu, Feng, 2023. "Asymmetric price transmission and impulse responses from U.S. crude oil to jet fuel and diesel markets," Energy, Elsevier, vol. 283(C).
    9. Tanrıverdi, Gökhan & Merkert, Rico & Karamaşa, Çağlar & Asker, Veysi, 2023. "Using multi-criteria performance measurement models to evaluate the financial, operational and environmental sustainability of airlines," Journal of Air Transport Management, Elsevier, vol. 112(C).
    10. Amaro, Raphael & Pinho, Carlos, 2022. "Energy commodities: A study on model selection for estimating Value-at-Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 5-27.
    11. Liu, Min & Liu, Hong-Fei & Lee, Chien-Chiang, 2024. "An empirical study on the response of the energy market to the shock from the artificial intelligence industry," Energy, Elsevier, vol. 288(C).
    12. Xie, Qichang & Fang, Tingwei & Rong, Xueyun & Xu, Xin, 2024. "Nonlinear behavior of tail risk resonance and early warning: Insight from global energy stock markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    13. Akyildirim, Erdinc & Corbet, Shaen & O'Connell, John F. & Sensoy, Ahmet, 2021. "The influence of aviation disasters on engine manufacturers: An analysis of financial and reputational contagion risks," International Review of Financial Analysis, Elsevier, vol. 74(C).
    14. Chen, Ruotian & Jiang, Changmin & Wang, Kun & Yang, Hangjun, 2024. "Engine option in aircraft purchase: Company strategies and policy implications," Transport Policy, Elsevier, vol. 159(C), pages 266-283.
    15. Gudmundsson, Sveinn Vidar & Merkert, Rico & Redondi, Renato, 2020. "Cost structure effects of horizontal airline mergers and acquisitions," Transport Policy, Elsevier, vol. 99(C), pages 136-144.
    16. Eyden Samunderu, 2023. "Jet Fuel Price Risk and Proxy Hedging in Spot Markets: A Two-Tier Model Analysis," Commodities, MDPI, vol. 2(3), pages 1-32, August.
    17. Shamita Garg & Vikas Thakur, 2021. "Modeling the Factors of Productivity in Airline Industry in India: A Hybrid TISM-P and Fuzzy-MICMAC Approach," International Journal of Global Business and Competitiveness, Springer, vol. 16(1), pages 39-47, December.
    18. Amaro, Raphael & Pinho, Carlos & Madaleno, Mara, 2022. "Forecasting the Value-at-Risk of energy commodities: A comparison of models and alternative distribution functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 77-101.
    19. Wolter, Alexander H. & Ehlers, Thorsten & Luetjens, Klaus & Gollnick, Volker, 2021. "Commodity price pass-through in the US airline industry and the hidden perks of consolidation," Journal of Air Transport Management, Elsevier, vol. 95(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0309975. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.