Information-Complexity Alignment for Stable Volatility Forecasting: A Model-Agnostic Framework with Regime Diagnostics

Information-Complexity Alignment for Stable Volatility Forecasting: A Model-Agnostic Framework with Regime Diagnostics | 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 Information-Complexity Alignment for Stable Volatility Forecasting: A Model-Agnostic Framework with Regime Diagnostics Li Yihan, Tian Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9060439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Forecasting financial volatility increasingly relies on complex models and high-dimensional information, yet greater complexity does not necessarily yield stable or interpretable behavior. Existing evaluation approaches emphasize predictive accuracy while offering limited insight into when and why complexity improves or destabilizes forecasting systems. This paper proposes the Information–Complexity Alignment Framework (ICAF), a model-agnostic approach that treats volatility forecasting as a dynamical system governed by the interaction between information availability and model complexity. Using daily Bitcoin data, we show that regime stability, boundedness, and temporal persistence emerge only when alignment is enforced through structural coupling, boundedness regulation, and memory. When these elements are relaxed, regimes fragment and persistence collapses despite unchanged data and models. By introducing alignment-driven geometric and temporal diagnostics beyond standard error metrics, ICAF provides a principled way to diagnose structural reliability in forecasting systems. The framework offers a general tool for evaluating when complexity enhances stability and when it amplifies instability in nonstationary environments. Information–Complexity Alignment Regime Dynamics Structural Stability Volatility Forecasting Model-Agnostic Diagnostics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 07 Mar, 2026 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-9060439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608803281,"identity":"31b62ab8-e31e-4ba7-96a2-84d6fdd0e59a","order_by":0,"name":"Li Yihan","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yihan","suffix":""},{"id":608803282,"identity":"ef49826b-de4b-4fed-bf13-de2a08ed1603","order_by":1,"name":"Tian Tian","email":"data:image/png;base64,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","orcid":"","institution":"Illinois Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Tian","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2026-03-07 19:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9060439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9060439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564418,"identity":"95ed2341-0e53-4495-9542-5fee42e9992e","added_by":"auto","created_at":"2026-03-27 12:49:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1799421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9060439/v1_covered_85991ac6-a43d-44cf-a217-9bb643a7a694.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Information-Complexity Alignment for Stable Volatility Forecasting: A Model-Agnostic Framework with Regime Diagnostics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Information–Complexity Alignment, Regime Dynamics, Structural Stability, Volatility Forecasting, Model-Agnostic Diagnostics","lastPublishedDoi":"10.21203/rs.3.rs-9060439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9060439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForecasting financial volatility increasingly relies on complex models and high-dimensional information, yet greater complexity does not necessarily yield stable or interpretable behavior. 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