Bayesian modeling of volatility in stock price using ARCH-GARCH with stan in R

Bayesian modeling of volatility in stock price using ARCH-GARCH with stan in R | 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 Bayesian modeling of volatility in stock price using ARCH-GARCH with stan in R Sayed Rahmi Khuda Haqbin, Athar Ali Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4924355/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 Stock price data naturally exhibits volatility. Specialized models are necessary to accurately represent and account for this volatile nature when modeling stock prices. This study employs a Bayesian approach to analyze ARCH and GARCH models applied to Apple stock price data. Leveraging the Stan probabilistic programming language within R, the research estimates model parameters and assesses volatility dynamics. Through Bayesian methods, the analysis incorporates prior information, enhancing parameter estimation and providing uncertainty measures for stock price forecasts. Markov chain Monte Carlo sampling techniques are utilized to obtain posterior distributions, revealing insights into volatility patterns, including clustering and persistence, within Apple stock prices. The study showcases the versatility of Bayesian techniques in financial econometrics, enabling model comparison and predictive performance assessment. Results offer valuable insights for investors, risk managers, and policymakers, aiding in understanding and managing stock price volatility. ARCH GARCH Bayesian MCMC LOOIC and WAIC STAN-R. Full Text Additional Declarations No competing interests reported. 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-4924355","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349354329,"identity":"5d4ff888-e0b6-4de0-b5bd-08bb4b4e96b1","order_by":0,"name":"Sayed Rahmi Khuda Haqbin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAwYGNoYEBhsefmbmA0C+hAyxWtLkJNvbEkBaeIjTwsBw2NjgzBkDkABhLebsh7c9eLiDObHhRs7nVzdqLHgY2A8f3YBPi2VPWrlB4hm2xMYZudusc44BHcaTlnYDr8MO5JhJJLbxJDZL5G4zzmEDapHgMcOv5fwbkBYQynlmnPOPGC03wLYYGPPwnGF+nNtGlJZnQL+0JchJsLeZMef2SfCwEfTL+eRtD3+2/eexP8z8+HPOtzo5fvbDx/BqYQBHDQSwSYBJAspRtDB/IEL1KBgFo2AUjEAAAAvqSRQBJR5kAAAAAElFTkSuQmCC","orcid":"","institution":"Aligarh Muslim University","correspondingAuthor":true,"prefix":"","firstName":"Sayed","middleName":"Rahmi Khuda","lastName":"Haqbin","suffix":""},{"id":349354330,"identity":"65f3adb0-c423-4eb6-a304-55985b7e88fe","order_by":1,"name":"Athar Ali Khan","email":"","orcid":"","institution":"Aligarh Muslim University","correspondingAuthor":false,"prefix":"","firstName":"Athar","middleName":"Ali","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-08-16 10:16:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4924355/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4924355/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68310218,"identity":"6a8832a9-6535-49ba-b0ef-029f3e7d3780","added_by":"auto","created_at":"2024-11-06 01:46:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":414427,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4924355/v1_covered_38474b9d-8cd3-4203-89d9-d92847368dc5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bayesian modeling of volatility in stock price using ARCH-GARCH with stan in R","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"ARCH, GARCH, Bayesian, MCMC, LOOIC and WAIC, STAN-R.","lastPublishedDoi":"10.21203/rs.3.rs-4924355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4924355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStock price data naturally exhibits volatility. Specialized models are necessary to accurately represent and account for this volatile nature when modeling stock prices. This study employs a Bayesian approach to analyze ARCH and GARCH models applied to Apple stock price data. Leveraging the Stan probabilistic programming language within R, the research estimates model parameters and assesses volatility dynamics. Through Bayesian methods, the analysis incorporates prior information, enhancing parameter estimation and providing uncertainty measures for stock price forecasts. Markov chain Monte Carlo sampling techniques are utilized to obtain posterior distributions, revealing insights into volatility patterns, including clustering and persistence, within Apple stock prices. The study showcases the versatility of Bayesian techniques in financial econometrics, enabling model comparison and predictive performance assessment. Results offer valuable insights for investors, risk managers, and policymakers, aiding in understanding and managing stock price volatility.\u003c/p\u003e","manuscriptTitle":"Bayesian modeling of volatility in stock price using ARCH-GARCH with stan in R","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-17 06:39:24","doi":"10.21203/rs.3.rs-4924355/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"b9335e5c-6f04-4074-bd24-68849a8fd244","owner":[],"postedDate":"September 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-06T01:38:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-17 06:39:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4924355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4924355","identity":"rs-4924355","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}