The Quest for Adaptive Inference: Comparing FC-TVPVAR and LSTM-TVPVAR in High-Dimensional Volatility Scenarios

The Quest for Adaptive Inference: Comparing FC-TVPVAR and LSTM-TVPVAR in High-Dimensional Volatility Scenarios | 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 The Quest for Adaptive Inference: Comparing FC-TVPVAR and LSTM-TVPVAR in High-Dimensional Volatility Scenarios Ozan Nadirgil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9106159/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The increasing complexity of financial contagion, combined with successive global crises, has intensified non-linear cross-market connections and the autoregressive conditional heteroscedasticity characteristics of financial time series, thereby exposing the limitations of constant variance models. In response, this study introduces a novel hybrid LSTM-TVP-VAR framework by combining Long Short Term Memory (LSTM) residual correction model with Time Varying Parameters Vector Auto Regression (TVP-VAR) to explore the cross-market non-linear connections and hidden temporal patterns across the cryptocurrency, commodity, technology, and clean energy markets. Performance results, including the Mean Total Connectedness Index (mTCI) value of 60.55%, Herfindahl-Hirschman Index (HHI) value of 0.0832, in addition to the Coefficient of Variation (CV) scores of 42.16% and 18.75% indicate that LSTM-TVP-VAR outperforms the FC-TVP-VAR in detecting non-linear connections, evenly allocating the volatility transmissions, and decreasing the sensitivity of results to idiosyncratic shocks from individual assets. Finally, Diebold-Mariano (DM) results verify the statistical significance of the contributions of the model and bootstrap test results validate the elimination of inherent over-fitting. Commodity Markets Deep Learning Energy Markets Equity Markets Financial Contagion Volatility Transmissions Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-9106159","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609247251,"identity":"c1bae80e-9101-4e74-8c27-084d8c4cc480","order_by":0,"name":"Ozan Nadirgil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYJACiQQGBh4+9gZStbDxHAAyE4jVAiLYQBqJ0sIv3fzwxsM9tTJskm+Pffj4wyaPQezwAbxaJOccM7ZIeHach006L3nmjIS0YgbpNPx2GdxIMJNIOHAMqCXHmJkn4XBig3SOAQEt6d8gWiTPwLTkfyCgJQdkSw0PmwQP3Ba8OhgkZ+QUWyQcOAAM5LxkxhlpaYlt0mn4HcYvkb7x5o8Ddfb87GcPM3ywsUnsl05+gN8aCDgMxDwQJhsx6oGgDqFlFIyCUTAKRgE6AADt70EVFRv4vQAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ozan","middleName":"","lastName":"Nadirgil","suffix":""}],"badges":[],"createdAt":"2026-03-12 14:53:38","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9106159/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-9106159/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108806233,"identity":"37a937de-797c-426e-b39e-d542940589b7","added_by":"auto","created_at":"2026-05-08 15:28:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16458529,"visible":true,"origin":"","legend":"","description":"","filename":"9A1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9106159/v2_covered_dea1189b-34a4-4e1a-bb74-b0182e331962.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"The Quest for Adaptive Inference: Comparing FC-TVPVAR and LSTM-TVPVAR in High-Dimensional Volatility Scenarios","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":"Commodity Markets; Deep Learning; Energy Markets; Equity Markets; Financial Contagion; Volatility Transmissions","lastPublishedDoi":"10.21203/rs.3.rs-9106159/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9106159/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing complexity of financial contagion, combined with successive global crises, has intensified non-linear cross-market connections and the autoregressive conditional heteroscedasticity characteristics of financial time series, thereby exposing the limitations of constant variance models.\u0026nbsp;In response, this study introduces a novel hybrid LSTM-TVP-VAR framework by combining Long Short Term Memory (LSTM)\u0026nbsp;residual correction model with\u0026nbsp;Time Varying Parameters Vector Auto Regression (TVP-VAR) to\u0026nbsp;explore the cross-market non-linear connections and hidden temporal patterns across the cryptocurrency, commodity, technology, and clean energy markets. Performance results, including the Mean Total Connectedness Index (mTCI) value of 60.55%, Herfindahl-Hirschman Index (HHI) value of 0.0832, \u0026nbsp;in addition to the Coefficient of Variation (CV) scores of 42.16% and 18.75% indicate that LSTM-TVP-VAR outperforms the FC-TVP-VAR in detecting non-linear connections, evenly allocating the volatility transmissions, and decreasing the sensitivity of results to idiosyncratic shocks from individual assets. Finally, Diebold-Mariano (DM) results verify the statistical significance of the contributions of the model and bootstrap test results validate the elimination of inherent over-fitting.\u003c/p\u003e","manuscriptTitle":"The Quest for Adaptive Inference: Comparing FC-TVPVAR and LSTM-TVPVAR in High-Dimensional Volatility Scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2026-05-06 17:47:25","doi":"10.21203/rs.3.rs-9106159/v2","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}},{"code":1,"date":"2026-03-20 05:43:08","doi":"10.21203/rs.3.rs-9106159/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":"85649c30-e515-42ff-b798-9572c2c3c089","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T05:43:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 17:47:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-9106159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9106159","identity":"rs-9106159","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}