Nonparametric Regime Segmentation in Financial Time Series via Hilbert–ICEEMDAN and Penalized Change-Point Inference

Nonparametric Regime Segmentation in Financial Time Series via Hilbert–ICEEMDAN and Penalized Change-Point Inference | 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 Nonparametric Regime Segmentation in Financial Time Series via Hilbert–ICEEMDAN and Penalized Change-Point Inference Devansh Garg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7877838/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 paper presents a novel methodological framework for detecting structural breaks in multivariate financial time series using Hilbert–ICEEMDAN decomposition combined with penalized change-point estimation. The approach decomposes 12 major financial assets over 17+ years (5,150 observations) into piecewise-stationary Intrinsic Mode Functions (IMFs) using ICEEMDAN, enabling well-defined instantaneous frequency extraction via Hilbert spectral analysis. IMF selection employs median period filtering within the 5-60 day range, with cross-asset aggregation through robust median-based combination of amplitude and frequency components. Change-point detection uses the PELT algorithm with least-squares cost function, penalty parameter β=5.0, and minimum segment length of 63 days. The framework identifies 57 distinct regime periods with median duration of 69 days, demonstrating consistency across parameter sensitivity analysis (125 combinations tested) and robustness diagnostics (9/9 validation checks passed). As an empirical illustration, regime-switched trading strategies achieve Sharpe ratios of 0.778 versus 0.381 for equalweight benchmarks, with walk-forward validation showing median Sharpe ratios of 1.155 for 5-year training periods. The system successfully captures major market events including the 2008 financial crisis, 2010 flash crash, 2011 European debt crisis, 2018 volatility spike, 2020 COVID crash, and 2022 Ukraine conflict. MSC 2020: 62M10, 62M20, 91G05, 62L12, 91G70 JEL: C22, C58, C14, C32, G11 Financial Mathematics Econometrics Finance Applied Mathematics Applied Statistics Financial regime detection ICEEMDAN Hilbert transform Change-point detection Time series analysis Market microstructure 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. 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-7877838","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530651598,"identity":"0c45a531-0b13-4a3b-a42e-1620a02dfaf9","order_by":0,"name":"Devansh Garg","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDCCAzCSmbGBgaECyGZmbiBaS2MDwxkGqF6itDAwMDYwtkFovDr4jvcYfi74c8eugZ25/dHNebXR/O1ALT8qtuHUInnmjLH0zLZnyQ1AhzXnbjueO+Mw0LaeM7dxajG4kZYgzdtwOJkBouVYbgNQCzNjGx4t958l/+b5A9My51jufIJabjAfk+ZhO2wH0dJQk7uBkBbJM8nHrHnbDiewAbXMzjl2IHcjUMtBfH7hO36w+TbQYfb8/McffM6pqcudd/7wwQc/KnBrgYHENgh9GEweIKgeCOyhdB0xikfBKBgFo2CEAQBYw2Fg46EDVgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0000-8210-2526","institution":"Indian Institute of Technology, Mandi","correspondingAuthor":true,"prefix":"","firstName":"Devansh","middleName":"","lastName":"Garg","suffix":""}],"badges":[],"createdAt":"2025-10-16 12:44:21","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-7877838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7877838/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93895303,"identity":"28eec95d-d2e1-4135-a5f2-9810f3ca364e","added_by":"auto","created_at":"2025-10-20 03:08:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1580284,"visible":true,"origin":"","legend":"","description":"","filename":"NonparametricRegimeSegmentationinFinancialTimeSeriesviaHilbertICEEMDANandPenalizedChangePointInference.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7877838/v1_covered_558b9a82-3075-44bb-83c7-7f813b2d4c71.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNonparametric Regime Segmentation in Financial Time Series via Hilbert–ICEEMDAN and Penalized Change-Point Inference\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":"Financial regime detection, ICEEMDAN, Hilbert transform, Change-point detection, Time series analysis, Market microstructure","lastPublishedDoi":"10.21203/rs.3.rs-7877838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7877838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a novel methodological framework for detecting structural breaks in multivariate financial time series using Hilbert–ICEEMDAN decomposition combined with penalized change-point estimation. 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