A multi-factor model for improved commodity pricing: Calibration and an application to the oil market

TL;DR

This paper introduces a comprehensive four-factor commodity pricing model that improves accuracy by capturing dynamic correlations and time-varying risk premiums, specifically applied to crude oil futures.

Contribution

It proposes a novel four-factor model integrating spot price, stochastic volatility, convenience yield, and stochastic interest rates, with a Kalman filter-based estimation framework.

Findings

Model effectively captures futures term structure complexities

Outperforms existing commodity pricing models

Demonstrates robustness in crude oil market analysis

Abstract

We present a new model for commodity pricing that enhances accuracy by integrating four distinct risk factors: spot price, stochastic volatility, convenience yield, and stochastic interest rates. While the influence of these four variables on commodity futures prices is well recognized, their combined effect has not been addressed in the existing literature. We fill this gap by proposing a model that effectively captures key stylized facts including a dynamic correlation structure and time-varying risk premiums. Using a Kalman filter-based framework, we achieve simultaneous estimation of parameters while filtering state variables through the joint term structure of futures prices and bond yields. We perform an empirical analysis focusing on crude oil futures, where we benchmark our model against established approaches. The results demonstrate that the proposed four-factor model effectively captures the complexities of futures term structures and outperforms existing models.