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Pricing of long-dated commodity derivatives: Do stochastic interest rates matter?

Author

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  • Cheng, Benjamin
  • Nikitopoulos, Christina Sklibosios
  • Schlögl, Erik
Abstract
Does modelling stochastic interest rates, beyond stochastic volatility, improve pricing performance on long-dated commodity derivatives? To answer this question, we consider futures price models for commodity derivatives that allow for stochastic volatility and stochastic interest rates and a correlation structure between the underlying variables. We examine the empirical pricing performance of these models on pricing long-dated crude oil derivatives. Estimating the model parameters from historical crude oil futures prices and option prices, we find that stochastic interest rate models improve pricing performance on long-dated crude oil derivatives, when the interest rate volatility is relatively high. Furthermore, increasing the model dimensionality does not tend to improve the pricing performance on long-dated crude oil option prices, but it matters for long-dated futures prices. We also find empirical evidence for a negative correlation between crude oil futures prices and interest rates that contributes to improving fit to long-dated crude oil option prices.

Suggested Citation

  • Cheng, Benjamin & Nikitopoulos, Christina Sklibosios & Schlögl, Erik, 2018. "Pricing of long-dated commodity derivatives: Do stochastic interest rates matter?," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 148-166.
    • Handle: RePEc:eee:jbfina:v:95:y:2018:i:c:p:148-166
    DOI: 10.1016/j.jbankfin.2017.05.012
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    2. Belssing Taruvinga, 2019. "Solving Selected Problems on American Option Pricing with the Method of Lines," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2019, January-A.
    3. Yu Feng & Ralph Rudd & Christopher Baker & Qaphela Mashalaba & Melusi Mavuso & Erik Schlögl, 2021. "Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models," Risks, MDPI, vol. 9(1), pages 1-20, January.
    4. Dai, Peng-Fei & Xiong, Xiong & Zhang, Jin & Zhou, Wei-Xing, 2022. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Resources Policy, Elsevier, vol. 78(C).
    5. Yu Feng, 2019. "Theory and Application of Model Risk Quantification," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2019, January-A.
    6. Zofia Gródek-Szostak & Gabriela Malik & Danuta Kajrunajtys & Anna Szeląg-Sikora & Jakub Sikora & Maciej Kuboń & Marcin Niemiec & Joanna Kapusta-Duch, 2019. "Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function," Sustainability, MDPI, vol. 11(15), pages 1-14, August.
    7. Hasan Dinçer & Serhat Yüksel & Fatih Pınarbaşı & Mehmet Ali Alhan, 2020. "Risky Financial Assets in Financial Integration and the Impacts of Derivatives on Banking Returns," World Scientific Book Chapters, in: Stéphane Goutte & Khaled Guesmi (ed.), Risk Factors and Contagion in Commodity Markets and Stocks Markets, chapter 6, pages 133-159, World Scientific Publishing Co. Pte. Ltd..
    8. Phan, Dinh Hoang Bach & Narayan, Paresh Kumar & Gong, Qiang, 2021. "Terrorist attacks and oil prices: Hypothesis and empirical evidence," International Review of Financial Analysis, Elsevier, vol. 74(C).
    9. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    10. Karol Binkowski & Peilun He & Nino Kordzakhia & Pavel Shevchenko, 2021. "On the Parameter Estimation in the Schwartz-Smiths Two-Factor Model," Papers 2108.01881, arXiv.org.
    11. Kang, Boda & Nikitopoulos, Christina Sklibosios & Prokopczuk, Marcel, 2020. "Economic determinants of oil futures volatility: A term structure perspective," Energy Economics, Elsevier, vol. 88(C).
    12. Jin, Jiayu & Han, Liyan & Xu, Yang, 2022. "Does the SDR stabilize investing in commodities?," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 160-172.
    13. Boda Kang & Christina Nikitopoulos Sklibosios & Erik Schlogl & Blessing Taruvinga, 2019. "The Impact of Jumps on American Option Pricing: The S&P 100 Options Case," Research Paper Series 397, Quantitative Finance Research Centre, University of Technology, Sydney.
    14. Blessing Taruvinga & Boda Kang & Christina Sklibosios Nikitopoulos, 2018. "Pricing American Options with Jumps in Asset and Volatility," Research Paper Series 394, Quantitative Finance Research Centre, University of Technology, Sydney.
    15. Ignatieva, Katja & Wong, Patrick, 2022. "Modelling high frequency crude oil dynamics using affine and non-affine jump–diffusion models," Energy Economics, Elsevier, vol. 108(C).
    16. P. Karlsson & K. F. Pilz & E. Schlögl, 2017. "Calibrating a market model with stochastic volatility to commodity and interest rate risk," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 907-925, June.

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    More about this item

    Keywords

    Futures options pricing; Stochastic interest rates; Correlations; Long-dated crude oil derivatives;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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