Volatility Forecasting with SVD Derived Covariance Features: A Deep Learning Approach

Volatility Forecasting with SVD Derived Covariance Features: A Deep Learning Approach | 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 Volatility Forecasting with SVD Derived Covariance Features: A Deep Learning Approach Ahmad Koman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7942322/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 Volatility forecasting remains a cornerstone of financial econometrics with applications in hedging, risk management, and asset allocation. While traditional models such as GARCH, HAR, and realized volatility frameworks capture temporal dependencies in single-asset volatility, they often fail to exploit information from the broader market’s cross-sectional structure. In contrast, the covariance matrix of asset returns encodes systemic co-movements that can serve as early indicators of market stress. Building on this insight, we propose a deep learning framework that enhances volatility prediction by integrating features derived from the singular value decomposition (SVD) of return covariance matrices. These features—such as the variance explained by leading principal components, generalized absorption ratios, and eigenvector alignment measures—capture the evolving interdependence and cohesion of financial markets. To ensure stability in high-dimensional settings, we estimate covariance matrices using Ledoit–Wolf shrinkage and feed the resulting SVD-based features, alongside traditional volatility predictors, into a neural network model. Empirical results on equity market data demonstrate that the proposed approach significantly outperforms benchmark models, including GARCH and HAR, in out-of-sample volatility forecasting. The findings highlight the predictive value of covariance-structure information and establish a pathway for combining market-wide factor dynamics with modern deep learning methods for improved volatility modeling. MSC Classification: 68T05 , 68T07 , 62P05 Volatility forecasting Covariance matrix Singular value decomposition Principal components Deep learning Absorption ratio Realized volatility 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-7942322","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539480439,"identity":"0c835eb6-2afa-4197-9e1a-680e3275e561","order_by":0,"name":"Ahmad Koman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCSDmOQBiMR8gWQtbApAwIEkLjwFxWvhndyd+eHPGJpp/ds+3xxUVf+TMGbgTH+C15M7ZzZJzbqTlzrhzdrvhmTMGxpYNvJvx23Ujd4M0z4fDuQ03crdJNrYZJG44wLtNAp8O+Ru5m3+DtMy/kfMMpmX7D3xaDICGS/PcOJy74UYOG9wWvO4yBGqxnHMmLXfjjTRzw4YzxsYGh3k343WYHNBhN94cs8mddyP52cOGCjk5g+O9Gz/gtQYJsEEoZmLVI7SMglEwCkbBKEADAHUBVRjurfG6AAAAAElFTkSuQmCC","orcid":"","institution":"Linnaeus University","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Koman","suffix":""}],"badges":[],"createdAt":"2025-10-25 22:42:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7942322/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7942322/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95836807,"identity":"bf4e016c-7bba-4341-9862-dc8134993567","added_by":"auto","created_at":"2025-11-13 13:28:32","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4551,"visible":true,"origin":"","legend":"","description":"","filename":"032301c4980e4297b9aa804a0865d3e5.json","url":"https://assets-eu.researchsquare.com/files/rs-7942322/v1/3163aa29f8f4230a9a3e0e91.json"},{"id":104399796,"identity":"2718e7e3-a3fb-4068-ab90-376a1130b398","added_by":"auto","created_at":"2026-03-11 12:07:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2769287,"visible":true,"origin":"","legend":"","description":"","filename":"SVDpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7942322/v1_covered_7c019d18-2429-4dc2-9475-fd1739458e15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Volatility Forecasting with SVD Derived Covariance Features: A Deep Learning Approach","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":"Volatility forecasting, Covariance matrix, Singular value decomposition; Principal components, Deep learning, Absorption ratio, Realized volatility","lastPublishedDoi":"10.21203/rs.3.rs-7942322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7942322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVolatility forecasting remains a cornerstone of financial econometrics with applications in hedging, risk management, and asset allocation. 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