Hybrid deep learning combined with traditional financial models: Application of RNN models and GARCH-Family Model for Natural Gas Price Volatility Forecasting

Hybrid deep learning combined with traditional financial models: Application of RNN models and GARCH-Family Model for Natural Gas Price Volatility Forecasting | 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 Hybrid deep learning combined with traditional financial models: Application of RNN models and GARCH-Family Model for Natural Gas Price Volatility Forecasting Yufeng Chen, Xingang Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4062752/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 The natural gas market has significant commonalities with the general financial market, especially its time series data are often non-stationary and show different fluctuation characteristics due to different market conditions. Therefore, accurate forecasting of natural gas price volatility requires a correct handling of the unique characteristics of its time series. In this paper, GARCH model and TGARCH model are specially selected to capture the volatility heteroscedasticity generated in different market scenarios, and IGARCH model is used to ensure that the model can still maintain high prediction accuracy when the time series is non-stationary. In order to deal with the long-term dependence of natural gas prices on time series, this paper introduces the LSTM model and the GRU model, both of which are variants of recurrent neural network (RNN). Thus we obtain the GARCH-IGARCH-TGARCH-LSTM/GRU model. It is worth noting that this model is applied to the field of natural gas price volatility prediction for the first time, which provides a new research perspective for in-depth understanding and accurate prediction of natural gas market volatility. We use the natural gas futures price index from June 2013 to June 2023 for the simulation test. Using 100 repeated experiments, we verify the robustness of the GARCH-IGARCH-TGARCH-GRU model in volatility forecasting and demonstrate its superior forecasting accuracy with a mean square error (MSE) of 0.22 and a mean absolute error (MAE) of 0.13. In the face of market breaks and extreme events, the integrated model shows higher adaptability and robustness. This study not only provides a powerful volatility forecasting tool for natural gas market participants, but also provides a strong demonstration of the universality of this type of model. Physical sciences/Energy science and technology/Fossil fuels Physical sciences/Mathematics and computing/Applied mathematics LSTM model GRU model GARCH model Natural Gas Price Volatility Machine Learning 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-4062752","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286754170,"identity":"e9fdf1ad-dd11-4936-a269-4a2148f8dcd1","order_by":0,"name":"Yufeng Chen","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Chen","suffix":""},{"id":286754171,"identity":"bbfd0096-7152-4f2a-b1b6-477ae322e0ea","order_by":1,"name":"Xingang Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBAC9gYQWQHhSBClhecAiDxDshbGNpK0sJ89Jvl13p3EDQeYD97mYbDLI6yFJy/ZWHbbM2ODA2zJ1jwMycUEtdgz5Bg+ltx2WM7gAI+ZNA/DgcQGgrbwvzE4LDnnMI/BAf5vRGqRyDF8+LEBbAsbsVreGBszHDtsLHmYzdhyjkEyMQ7LMZP8UXM4se9488MbbyrsCGsBAWYeMAkiDIhRDwSMP4hUOApGwSgYBSMUAACVvDe2lLQYIAAAAABJRU5ErkJggg==","orcid":"","institution":"Ningxia University","correspondingAuthor":true,"prefix":"","firstName":"Xingang","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2024-03-10 07:29:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4062752/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4062752/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58384196,"identity":"c206b84e-60c0-4a91-84b0-b23736894568","added_by":"auto","created_at":"2024-06-14 18:31:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1379792,"visible":true,"origin":"","legend":"","description":"","filename":"LSTMandGRUGARCHModelsforVolatilityForecasting.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4062752/v1_covered_2a5137c4-9444-40b8-a122-55356388a8f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid deep learning combined with traditional financial models: Application of RNN models and GARCH-Family Model for Natural Gas Price Volatility Forecasting","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":"LSTM model, GRU model, GARCH model, Natural Gas Price Volatility, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4062752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4062752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe natural gas market has significant commonalities with the general financial market, especially its time series data are often non-stationary and show different fluctuation characteristics due to different market conditions. 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