Measurement of Systemic Risk Based on the QRDCCNN Model

Measurement of Systemic Risk Based on the QRDCCNN Model | 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 Measurement of Systemic Risk Based on the QRDCCNN Model JUCHAO LI, JILIANG SHENG, YI HUANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3988882/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 Measuring and preventing systemic risk have always been core issues in finance. To accurately capture systemic risk, this is the first introduction of the Quantile Regression Dilated Causal Convolution Neural Network (QRDCCNN) model for assessing systemic risk. This model focuses on the causal consistency of financial time series and effectively expands the model's receptive field by increasing the dilation rate layer by layer. The study selects the daily closing prices of the S\&P 500 index and 38 US financial institutions as subjects. The QRDCCNN model is employed to measure the VaR of each financial institution and the CoVaR of the financial system when these institutions are in extreme risk conditions. This paper compares the results of the QRDCCNN model with those from the DCC-GARCH, quantile regression, QRNN, and QRCNN models using the Kupiec test. The research results show that the QRDCCNN model has the highest accuracy, followed by QRNN and QRCNN models, while the DCC-GARCH model has the lowest accuracy. Systemic risk Quantile regression Convolution neural network Dilated causal convolution Full Text 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-3988882","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326338256,"identity":"d2705ada-f619-4961-9bf6-6e898c675bb0","order_by":0,"name":"JUCHAO LI","email":"data:image/png;base64,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","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"JUCHAO","middleName":"","lastName":"LI","suffix":""},{"id":326338257,"identity":"46fc2ce0-c398-475f-8065-31a9ecc139b8","order_by":1,"name":"JILIANG SHENG","email":"","orcid":"https://orcid.org/0000-0002-2890-4090","institution":"Jiangxi University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"JILIANG","middleName":"","lastName":"SHENG","suffix":""},{"id":326338258,"identity":"133ab822-bdee-40e8-9751-77938e1a2834","order_by":2,"name":"YI HUANG","email":"","orcid":"","institution":"Jiangxi University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"YI","middleName":"","lastName":"HUANG","suffix":""}],"badges":[],"createdAt":"2024-02-25 19:57:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3988882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3988882/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63833944,"identity":"0316c8b5-9abf-4f64-abaa-f86838c36003","added_by":"auto","created_at":"2024-09-02 19:47:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2040525,"visible":true,"origin":"","legend":"","description":"","filename":"SOCOD2401005.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3988882/v1_covered_618fa6eb-fcf0-4110-9ccb-3dff28e10c6f.pdf"}],"financialInterests":"","formattedTitle":"Measurement of Systemic Risk Based on the QRDCCNN Model","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":"Systemic risk, Quantile regression, Convolution neural network, Dilated causal convolution","lastPublishedDoi":"10.21203/rs.3.rs-3988882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3988882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMeasuring and preventing systemic risk have always been core issues in finance. 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