Valuation of commodity option prices under a regime-switching model with stochastic convenience yield: Model calibration using flower pollination optimization algorithm

This research work seeks to construct a model for commodity spot prices by incorporating the concept of stochastic convenience yield within a Markov-switching framework. The model presented in this paper applies the Gibson-Schwartz commodity model under the risk-neutral measure, enabling regime-switching in the convenience yield and spot price dynamics. Using the WTI crude oil spot prices, the parameters involved in the proposed commodity regime-switching model are estimated by expectation–maximization algorithm. We then carry out a semi-analytical formula for the commodity futures contracts and European option price written on them. We calibrate the option pricing model parameters using the flower pollination optimization algorithm based on the European call option prices in WTI crude oil market. The results show that the provided Markov-switching model, whose parameters are calibrated by the flower pollination optimization algorithm is superior to the some common models in commodity literature. <h2>Other Information</h2> Published in: Journal of Computational and Applied Mathematics<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cam.2025.117150" target="_blank">https://dx.doi.org/10.1016/j.cam.2025.117150</a>

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Publication Details

Journal
Journal of Computational and Applied Mathematics
Published
2025-10-10
DOI
https://doi.org/10.1016/j.cam.2025.117150
Primary Topic
Capital Investment and Risk Analysis
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article
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article

Valuation of commodity option prices under a regime-switching model with stochastic convenience yield: Model calibration using flower pollination optimization algorithm

Idin Noorani, Farshid Mehrdoust, H. Smaoui, Ilyasse Aksikas et al.
Journal of Computational and Applied Mathematics
Capital Investment and Risk Analysis
article

Valuation of commodity option prices under a regime-switching model with stochastic convenience yield: Model calibration using flower pollination optimization algorithm

Idin Noorani, Farshid Mehrdoust, H. Smaoui, Ilyasse Aksikas, Abdelouahed Hamdi
article en

Abstract

This research work seeks to construct a model for commodity spot prices by incorporating the concept of stochastic convenience yield within a Markov-switching framework. The model presented in this paper applies the Gibson-Schwartz commodity model under the risk-neutral measure, enabling regime-switching in the convenience yield and spot price dynamics. Using the WTI crude oil spot prices, the parameters involved in the proposed commodity regime-switching model are estimated by expectation–maximization algorithm. We then carry out a semi-analytical formula for the commodity futures contracts and European option price written on them. We calibrate the option pricing model parameters using the flower pollination optimization algorithm based on the European call option prices in WTI crude oil market. The results show that the provided Markov-switching model, whose parameters are calibrated by the flower pollination optimization algorithm is superior to the some common models in commodity literature. <h2>Other Information</h2> Published in: Journal of Computational and Applied Mathematics<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cam.2025.117150" target="_blank">https://dx.doi.org/10.1016/j.cam.2025.117150</a>

Journal of Computational and Applied MathematicsVol. 476
Openalex Percentile: Top 63%
Capital Investment and Risk Analysis
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Valuation of commodity option prices under a regime-switching model with stochastic convenience yield: Model calibration using flower pollination optimization algorithm — Idin Noorani, Farshid Mehrdoust, et al. · Journal of Computational and Applied Mathematics (2025) | TGRS Research Map | TGRS