AI-Driven Cyber Risks and Financial Market Stability: Evidence from Volatility Markets and Risk Spillovers

AI-Driven Cyber Risks and Financial Market Stability: Evidence from Volatility Markets and Risk Spillovers | 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 Article AI-Driven Cyber Risks and Financial Market Stability: Evidence from Volatility Markets and Risk Spillovers Kiran Kumar Katepogu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9195505/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The paper aims to investigate the effects of artificial intelligence (AI)-related cyber risks on financial market volatility, systemic spillovers, and forecasting performance for the banking sector. The study used daily stock return data for prominent Indian banking institutions over the period 2014-2025. A cyber risk index is constructed based on Google Trends data for cyber-related search intensity. The financial market volatility is analyzed using a GARCH model that incorporates the cyber risk index into the conditional variance equation. The systemic spillovers between financial institutions are examined using a vector autoregression (VAR) model. Additionally, the forecasting performance is also assessed using a Long Short-Term Memory (LSTM) neural network. The results from the empirical analysis indicate that cyber risks do indeed increase conditional market volatility. The results from the spillover analysis indicate that there is a high level of systematic interconnectedness between financial institutions. This is shown by a high value for the spillover index, which is approximately 89.98%. The results from the forecast evaluation indicate that the LSTM model does indeed outperform the traditional GARCH model. This is shown by lower values for the RMSE and MASE metrics, as well as statistically significant Diebold-Mariano test statistics -9.692. This reveals the increasing importance that cyber risks are having on financial instability. It also shows that the integration of AI-based forecasting techniques into traditional financial models does help in the evaluation of cyber-related financial risks. JEL Codes: G10, G21, C58, G28, G32 Business and commerce/Economics Social science/Economics Business and commerce/Finance Social science/Finance Physical sciences/Mathematics and computing Cyber risk financial volatility Systematic Risk artificial intelligence GARCH and LSTM Full Text Additional Declarations No competing interests reported. Supplementary Files CodeAIDriven.py Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Apr, 2026 Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor invited by journal 30 Mar, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 23 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-9195505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615500655,"identity":"15ee7b6d-817c-4d3f-94ef-edd94c5b8941","order_by":0,"name":"Kiran Kumar Katepogu","email":"data:image/png;base64,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","orcid":"","institution":"Kristu Jayanti (Deemed to be University)","correspondingAuthor":true,"prefix":"","firstName":"Kiran","middleName":"Kumar","lastName":"Katepogu","suffix":""}],"badges":[],"createdAt":"2026-03-23 04:40:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9195505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9195505/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106402871,"identity":"7cc36e53-8075-4631-88c2-ccd236ac39e1","added_by":"auto","created_at":"2026-04-08 09:13:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":423445,"visible":true,"origin":"","legend":"","description":"","filename":"NewAIDrivenCyberRisks1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9195505/v1_covered_12c5528d-a921-4b44-8667-bc6cf740e935.pdf"},{"id":106221091,"identity":"d4bbafb5-c490-4186-8787-b9457b2596d4","added_by":"auto","created_at":"2026-04-06 09:49:54","extension":"py","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5801,"visible":true,"origin":"","legend":"","description":"","filename":"CodeAIDriven.py","url":"https://assets-eu.researchsquare.com/files/rs-9195505/v1/bdca6157d5dd9240f807e62c.py"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Driven Cyber Risks and Financial Market Stability: Evidence from Volatility Markets and Risk Spillovers","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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cyber risk, financial volatility, Systematic Risk, artificial intelligence, GARCH and LSTM","lastPublishedDoi":"10.21203/rs.3.rs-9195505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9195505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe paper aims to investigate the effects of artificial intelligence (AI)-related cyber risks on financial market volatility, systemic spillovers, and forecasting performance for the banking sector. 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