A. Hamdi , I. Aksikas , H. Smaoui , F. Mehrdoust , I. Noorani
{"title":"具有随机便利产量的制度交换模型下的商品期权价格估值:用授粉优化算法对模型进行标定","authors":"A. Hamdi , I. Aksikas , H. Smaoui , F. Mehrdoust , I. Noorani","doi":"10.1016/j.cam.2025.117150","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"476 ","pages":"Article 117150"},"PeriodicalIF":2.6000,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Valuation of commodity option prices under a regime-switching model with stochastic convenience yield: Model calibration using flower pollination optimization algorithm\",\"authors\":\"A. Hamdi , I. Aksikas , H. Smaoui , F. Mehrdoust , I. Noorani\",\"doi\":\"10.1016/j.cam.2025.117150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50226,\"journal\":{\"name\":\"Journal of Computational and Applied Mathematics\",\"volume\":\"476 \",\"pages\":\"Article 117150\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational and Applied Mathematics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0377042725006648\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational and Applied Mathematics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0377042725006648","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
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.
期刊介绍:
The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest.
The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.