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

Peilun He, Karol Binkowski, Nino Kordzakhia and Pavel Shevchenko

Papers from arXiv.org

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.

Date: 2021-08
New Economics Papers: this item is included in nep-ene, nep-ets and nep-isf
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