Machining, the Better – A Look at Machine Learning-Based Volatility Using the SVR-GARCH for Frontier Market Equities

Machining, the Better – A Look at Machine Learning-Based Volatility Using the SVR-GARCH for Frontier Market Equities | 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 Machining, the Better – A Look at Machine Learning-Based Volatility Using the SVR-GARCH for Frontier Market Equities Carl Hope Korkpoe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7152394/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 Financial market volatility is a critical factor influencing investment decisions, risk management, and economic policy. Traditional statistical models often struggle to capture the complex, nonlinear relationships in financial data, leading to suboptimal volatility forecasting. Machine learning (ML) techniques presently offer a promising alternative by leveraging vast amounts of historical data to identify hidden patterns and improve predictive accuracy. We explore the use of a hybrid Supervised Vector Machine (SVM) -GARCH algorithm against the various variant of GARCH to study the heteroscedasticity of the equity returns drawn from the Ghana Stock Exchange (GSE) as typically representing frontier markets in sub-Saharan African markets. We compared the effectiveness of these hybrid models to the classical GARCH and highlighted the advantages of data-driven ML models in adapting to dynamic market conditions. Our findings demonstrate that ML-based models can enhance forecasting performance, reduce estimation errors, and provide deeper insights into market behavior, making them valuable tools for both investors and policymakers in frontier and developed markets alike . Finance Sub-Saharan Africa machine learning volatility GARCH Full Text Additional Declarations The authors declare no competing interests. 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. 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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-7152394","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487108652,"identity":"dfd71fd9-1ee7-41ef-ae49-bcc8fa1195cd","order_by":0,"name":"Carl Hope Korkpoe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACHgYGxgYgzQ/hMkOEiNIi2UCyFoMDxGrh7zl87OPMHTZyxjeSH35gqLBObOBfewCvFomzbckzN55JMza7kWYswXAmPbFB4l0CXi0G/DzGjA/bDiduu5HDxsAIZDRInDEgSkv95hkgLf+I0cLbY8y4se1wgoEESEsDUAt/D34tEmeOJTPOPJNmOOPMM2OJhGPpxm0SPPi18PckH2bs3WEjz98ODLEPNday/fwEHAYG4IgBgQQgZpNIIKwDoQVi8QEitIyCUTAKRsFIAgBdxUWKfB9KnwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Cape Coast","correspondingAuthor":true,"prefix":"","firstName":"Carl","middleName":"Hope","lastName":"Korkpoe","suffix":""}],"badges":[],"createdAt":"2025-07-17 22:20:35","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-7152394/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7152394/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87181020,"identity":"83fddf75-31ee-405f-8626-8d32f12826d8","added_by":"auto","created_at":"2025-07-21 09:44:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":721781,"visible":true,"origin":"","legend":"","description":"","filename":"MachiningtheBetterMachineLearningVolatilityFinancialandPortfolioJournal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7152394/v1_covered_fb9ef862-b61d-430c-ba77-69fbc7ba54dc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMachining, the Better – A Look at Machine Learning-Based Volatility Using the SVR-GARCH for Frontier Market Equities\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Sub-Saharan Africa, machine learning volatility, GARCH","lastPublishedDoi":"10.21203/rs.3.rs-7152394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7152394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eFinancial market volatility is a critical factor influencing investment decisions, risk management, and economic policy. Traditional statistical models often struggle to capture the complex, nonlinear relationships in financial data, leading to suboptimal volatility forecasting. Machine learning (ML) techniques presently offer a promising alternative by leveraging vast amounts of historical data to identify hidden patterns and improve predictive accuracy. We explore the use of a hybrid Supervised Vector Machine (SVM) -GARCH algorithm against the various variant of GARCH to study the heteroscedasticity of the equity returns drawn from the Ghana Stock Exchange (GSE) as typically representing frontier markets in sub-Saharan African markets. We compared the effectiveness of these hybrid models to the classical GARCH and highlighted the advantages of data-driven ML models in adapting to dynamic market conditions. 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