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

We present a new approach to commodity pricing that enhances accuracy by integrating four distinct risk factors: the spot price, stochastic volatility, convenience yield, and stochastic interest rates. We build on Yan [Valuation of commodity derivatives in a new multi-factor model. Rev. Deriv. Res., 2002, 5, 251–271], the only model to our knowledge that incorporates all four sources of risk, and extend it by adding a more flexible correlation structure that captures state-dependent co-movements and time-varying risk premia. A further contribution is the explicit inclusion of the stochastic interest-rate factor within a unified Kalman-filter framework, which allows us to jointly filter the state variables and estimate model parameters using both commodity and bond market data. An empirical analysis of crude-oil futures shows that our four-factor model captures the complex dynamics of the futures term structure and consistently outperforms existing benchmarks.

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Publication Details

Journal
Quantitative Finance
Published
2026-02-17
DOI
https://doi.org/10.1080/14697688.2026.2619531
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
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article

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

Quantitative Finance
Market Dynamics and Volatility
article

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

article en

Abstract

We present a new approach to commodity pricing that enhances accuracy by integrating four distinct risk factors: the spot price, stochastic volatility, convenience yield, and stochastic interest rates. We build on Yan [Valuation of commodity derivatives in a new multi-factor model. Rev. Deriv. Res., 2002, 5, 251–271], the only model to our knowledge that incorporates all four sources of risk, and extend it by adding a more flexible correlation structure that captures state-dependent co-movements and time-varying risk premia. A further contribution is the explicit inclusion of the stochastic interest-rate factor within a unified Kalman-filter framework, which allows us to jointly filter the state variables and estimate model parameters using both commodity and bond market data. An empirical analysis of crude-oil futures shows that our four-factor model captures the complex dynamics of the futures term structure and consistently outperforms existing benchmarks.

Quantitative FinanceVol. 26(3)
University of Bologna (IT)
Decent work and economic growth
Openalex Percentile: Top 56%
Market Dynamics and Volatility
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