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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
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    References listed on IDEAS

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    1. Vinogradov, Dmitry V., 2012. "Arbitrary truncated Levy flight: Asymmetrical truncation and high-order correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5584-5597.
    2. Samet Gunay & Audil Rashid Khaki, 2018. "Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models," JRFM, MDPI, vol. 11(2), pages 1-19, June.
    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. Pernagallo, Giuseppe & Torrisi, Benedetto, 2019. "An empirical analysis on the degree of Gaussianity and long memory of financial returns in emerging economies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    5. Calzolari, Giorgio & Halbleib, Roxana & Parrini, Alessandro, 2014. "Estimating GARCH-type models with symmetric stable innovations: Indirect inference versus maximum likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 158-171.
    6. Zhou, Wei & Gu, Qinen & Chen, Jin, 2021. "From volatility spillover to risk spread: An empirical study focuses on renewable energy markets," Renewable Energy, Elsevier, vol. 180(C), pages 329-342.
    7. 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.
    8. Swidan, Hassan & Merkert, Rico, 2019. "The relative effect of operational hedging on airline operating costs," Transport Policy, Elsevier, vol. 80(C), pages 70-77.
    9. Kwangil Bae & Hankil Kang & Jangkoo Kang, 2020. "Can fat-tail create the momentum and reversal?," Applied Economics, Taylor & Francis Journals, vol. 52(44), pages 4850-4863, September.
    10. 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.
    11. 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.
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