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On Modelling of Crude Oil Futures in a Bivariate State-Space Framework

Author

Listed:
  • Peilun He
  • Karol Binkowski
  • Nino Kordzakhia
  • Pavel Shevchenko

Abstract

We study a bivariate latent factor model for the pricing of commodity fu- tures. The two unobservable state variables representing the short and long term fac- tors are modelled as Ornstein-Uhlenbeck (OU) processes. The Kalman Filter (KF) algorithm has been implemented to estimate the unobservable factors as well as unknown model parameters. The estimates of model parameters were obtained by maximising a Gaussian likelihood function. The algorithm has been applied to WTI Crude Oil NYMEX futures data.

Suggested Citation

  • Peilun He & Karol Binkowski & Nino Kordzakhia & Pavel Shevchenko, 2021. "On Modelling of Crude Oil Futures in a Bivariate State-Space Framework," Papers 2108.01886, arXiv.org.
    • Handle: RePEc:arx:papers:2108.01886
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    References listed on IDEAS

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    5. Christian-Oliver Ewald & Aihua Zhang & Zhe Zong, 2019. "On the calibration of the Schwartz two-factor model to WTI crude oil options and the extended Kalman Filter," Annals of Operations Research, Springer, vol. 282(1), pages 119-130, November.
    6. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    7. 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.
    8. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
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