A Theoretical Framework for Crude Oil Price Evolution: Insights from the Financial Crisis and Beyond

A Theoretical Framework for Crude Oil Price Evolution: Insights from the Financial Crisis and Beyond | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Theoretical Framework for Crude Oil Price Evolution: Insights from the Financial Crisis and Beyond houssam boughabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7133036/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study develops a theoretical model to understand the dynamics of crude oil prices, integrating Keynesian insights on imperfect competition and longmemory volatility through the FIGARCH framework. The model incorporates both demand and supply-side factors, with a particular focus on firm expectations and production costs, to explain price fluctuations. By calibrating the model to historical oil price data, we examine how demand dynamics, driven by expectations of future demand and current production costs, influence oil price movements. The study highlights the limitations of relying solely on demand as a predictor for price changes, particularly in the context of global disruptions such as the COVID-19 pandemic. Our results reveal that the exclusion of supply-side factors, including production costs and geopolitical risks, leads to significant discrepancies in price predictions, especially during periods of crisis. The findings emphasize the need for a more comprehensive approach to modeling oil prices, incorporating both demand and supply dynamics, to better capture market behavior during times of global shocks. JEL Classification. C58, G13, Q41. Macroeconomics Long Memory Volatility Market Efficiency Covid Pandemic Price Dynamics Theoretical Modeling Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Codepythonmodel.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7133036","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485917687,"identity":"e7953d16-737a-400c-af7c-d4d6c7a25341","order_by":0,"name":"houssam boughabi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAwYeEHWAwYCZ+cABUrWwJZCqhYHHgDiHmTPwHnzM8+uOvDk7z8fDBX9s5BnYDz9gulGDW4tlA1+yMW/fM8OdzbwbDs9sSzNs4EkzYM45hsdhB3jMpHl7DjNuOAzUwttwmLGBIYeBOYeNsBb7DYd5Hhzm+fPfvoH/DVDLPwJaeH4cTgRqYTjMw3YgsUECaEtuGx4th/mSDec2HE7e2cxmcJi3LTm5TeKZweHcPjxajvcefPDmz2Hb7fyHH3/m+WNn28+f/PBxzjfcWhiYgZgR2Rkgjx/AowEK/hBWMgpGwSgYBSMYAABudFObf+hDdwAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Statistics and Applied Economics","correspondingAuthor":true,"prefix":"","firstName":"houssam","middleName":"","lastName":"boughabi","suffix":""}],"badges":[],"createdAt":"2025-07-15 17:36:13","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7133036/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7133036/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86882589,"identity":"11b10a50-1593-4f6e-b52c-0b98c303814c","added_by":"auto","created_at":"2025-07-16 16:53:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":310074,"visible":true,"origin":"","legend":"","description":"","filename":"Article30.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7133036/v1_covered_5b0ff31c-49e2-4bf7-8a90-cc01ff8be77a.pdf"},{"id":86882456,"identity":"9e886f3c-9dad-4b5d-8a7c-d6c288ec0695","added_by":"auto","created_at":"2025-07-16 16:45:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19597,"visible":true,"origin":"","legend":"","description":"","filename":"Codepythonmodel.docx","url":"https://assets-eu.researchsquare.com/files/rs-7133036/v1/b1ae4f2491c6abee788814dd.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Theoretical Framework for Crude Oil Price Evolution: Insights from the Financial Crisis and Beyond\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Institute of Statistics and Applied Economics","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Long Memory Volatility, Market Efficiency, Covid Pandemic, Price Dynamics, Theoretical Modeling","lastPublishedDoi":"10.21203/rs.3.rs-7133036/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7133036/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study develops a theoretical model to understand the dynamics of crude oil prices, integrating Keynesian insights on imperfect competition and longmemory volatility through the FIGARCH framework. 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