Monte-Carlo Simulation Based Value-at-Risk for Non-Gaussian Seasonal Stochastic Volatility Model

Monte-Carlo Simulation Based Value-at-Risk for Non-Gaussian Seasonal Stochastic Volatility 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 Monte-Carlo Simulation Based Value-at-Risk for Non-Gaussian Seasonal Stochastic Volatility Model Yongbo SUN, Zhengjun JIANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4090690/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 Commodity option has relatively low correlations with equities and bonds and is a good diversification asset to a portfolio compared with traditional assets. However, commodity has seasonal patterns compared with other assets. In this article, we combine stochastic volatility model with seasonal patterns and do risk measurement such as calculating options' value-at-risk (VaR). We also study non-Gaussian stochastic volatility model in student \(t\) distribution and skew-student- $ t $ distribution instead of usual Gaussian distribution which take skewness and fat tails into consideration with tail losses and extreme events typical of commodity markets. Our results demonstrate that non-Gaussian distributed seasonal stochastic volatility model can better estimate VaR and has higher probability that extreme cases may happen. This research suggests that our model can serve as a powerful tool for investors seeking to manage risks more effectively in volatile commodity markets, highlighting the importance of considering both seasonal influences and distributional characteristics in financial modeling. Commodity option seasonal stochastic volatility model non-Gaussian distribution value-at-risk (VaR) Full Text Additional Declarations No competing interests reported. Supplementary Files gasolinefuturesdata.xlsx oilfuturesdatafull.xlsx CrudeOilCallJan.xlsx GasolineCallJan.xlsx 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-4090690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281897899,"identity":"58bd6bdc-81da-4c84-b879-609f4e421400","order_by":0,"name":"Yongbo SUN","email":"","orcid":"","institution":"Faculty of Science and Technology, BNU-HKBU United International College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongbo","middleName":"","lastName":"SUN","suffix":""},{"id":281897902,"identity":"f44bdb98-133e-446e-a492-6fee9ba11a23","order_by":1,"name":"Zhengjun 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(VaR)","lastPublishedDoi":"10.21203/rs.3.rs-4090690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4090690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCommodity option has relatively low correlations with equities and bonds and is a good diversification asset to a portfolio compared with traditional assets. However, commodity has seasonal patterns compared with other assets. In this article, we combine stochastic volatility model with seasonal patterns and do risk measurement such as calculating options' value-at-risk (VaR). We also study non-Gaussian stochastic volatility model in student \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(t\\) \u003c/span\u003e \u003c/span\u003e distribution and skew-student-\u003cspan\u003e$\u003c/span\u003et\u003cspan\u003e$\u003c/span\u003e distribution instead of usual Gaussian distribution which take skewness and fat tails into consideration with tail losses and extreme events typical of commodity markets. Our results demonstrate that non-Gaussian distributed seasonal stochastic volatility model can better estimate VaR and has higher probability that extreme cases may happen. This research suggests that our model can serve as a powerful tool for investors seeking to manage risks more effectively in volatile commodity markets, highlighting the importance of considering both seasonal influences and distributional characteristics in financial modeling.\u003c/p\u003e","manuscriptTitle":"Monte-Carlo Simulation Based Value-at-Risk for Non-Gaussian Seasonal Stochastic Volatility Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 16:59:17","doi":"10.21203/rs.3.rs-4090690/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"7b918b50-999b-4fc7-9d92-4d03c7908a36","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-29T20:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-22 16:59:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4090690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4090690","identity":"rs-4090690","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}