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

TL;DR

This paper develops a bivariate state-space model with Ornstein-Uhlenbeck processes for crude oil futures, employing Kalman filtering to estimate unobservable factors and parameters from market data.

Contribution

It introduces a novel bivariate latent factor model for commodity futures pricing and applies Kalman filtering for parameter estimation using real market data.

Findings

Successful estimation of unobservable factors and parameters from WTI Crude Oil futures data.

Demonstrates the effectiveness of the bivariate OU process model in capturing futures dynamics.

Provides a framework for improved pricing and risk management of commodity futures.

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.