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

Peilun He (), Karol Binkowski (), Nino Kordzakhia () and Pavel Shevchenko ()
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Peilun He: Macquarie University
Karol Binkowski: Macquarie University
Nino Kordzakhia: Macquarie University
Pavel Shevchenko: Macquarie University

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2021, pp 273-278 from Springer

Abstract: Abstract We study a bivariate latent factor model for the pricing of commodity futures. The two unobservable state variables representing the short and long term factors 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.

Keywords: Kalman filter; Kalman smoother; State-space model; Crude oil futures (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78965-7_40

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DOI: 10.1007/978-3-030-78965-7_40

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