Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era

Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era | 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 Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era Zaghum Umar, Francisco Jareño, Ana M. Escribano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-75766/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Sep, 2021 Read the published version in Applied Economics → Version 1 posted You are reading this latest preprint version Abstract This paper explores the dynamic return and volatility connectedness for the three most relevant agricultural commodity markets (Soft, Grain and Livestock) and the Coronavirus Media Coverage Index (MCI) extracted from RavenPack. In concrete, we apply the fresh TVP-VAR methodology proposed by Antonakakis and Gabauer (2017) during the sample period between January 22, 2020 and July 31, 2020, that is in the context of the COVID-19 pandemic crisis. Interesting results are found in this research. First, dynamic total return and volatility connectedness fluctuates over time, reaching a peak during the heart of the global pandemic crisis, initially in the dynamic total return connectedness and later in the volatility one. Second, in the dynamic connectedness TO the system, we observe significant differences between the agricultural commodity markets in the level of the return connectedness measure. However, in the dynamic volatility connectedness TO, there are very few differences between some elements of the system, highlighting the Coronavirus MCI as one of the major transmitters TO the system at some points in the sample. This Coronavirus MCI appears as the less relevant receiver FROM the system, not only in terms of dynamic return connectedness, but also in volatility. Finally, regarding the net dynamic total connectedness, the Coronavirus MCI shows the highest values in return and volatility. Next, in order of highest to lowest net dynamic return and volatility connectedness we find the Grain commodity market, then the Soft market and finally the Livestock market. This last commodity market shows a negative net dynamic connectedness throughout the entire sample period analysed, exhibiting negative peaks in return at the beginning of the pandemic’s epicentre and in volatility in the middle of the COVID-19 crisis. Finance International Economics Agricultural commodity markets Coronavirus Media Coverage Index (MCI) Connectedness COVID-19 pandemic crisis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Declarations DATA AVAILABILITY STATEMENT The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. COMPETING INTERESTS The authors declare that they have no competing interests. FUNDING This work was supported by the Spanish Ministerio de Economía, Industria y Competitividad (ECO2017-89715-P). AUTHORS' CONTRIBUTIONS Zaghum Umar (ZU): Conceptualization, Data curation, Methodology, Software, Supervision. Francisco Jareño (FJ): Visualization, Investigation, Writing- Original draft preparation, Validation, Writing- Reviewing and Editing, Funding acquisition, Formal analysis. Ana M. Escribano (AME): Visualization, Investigation, Writing- Original draft preparation, Validation, Writing- Reviewing and Editing, Funding acquisition, Formal analysis. Table Cite Share Download PDF Status: Published Journal Publication published 05 Sep, 2021 Read the published version in Applied Economics → 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. 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INTERESTS\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFUNDING\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Spanish \u003cem\u003eMinisterio de Econom\u0026iacute;a, Industria y Competitividad\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(ECO2017-89715-P).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUTHORS' CONTRIBUTIONS\u003c/p\u003e\n\u003cp\u003eZaghum Umar (ZU): Conceptualization, Data curation, Methodology, Software, Supervision.\u003c/p\u003e\n\u003cp\u003eFrancisco Jare\u0026ntilde;o (FJ): Visualization, Investigation, Writing- Original draft preparation, Validation, Writing- Reviewing and Editing, Funding acquisition, Formal analysis.\u003c/p\u003e\n\u003cp\u003eAna M. Escribano (AME): Visualization, Investigation, Writing- Original draft preparation, Validation, Writing- Reviewing and Editing, Funding acquisition, Formal analysis.\u003c/p\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58677_ec8811c6b4185256/58677_custom_files/img1599837885.png\"\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"Agricultural commodity markets, Coronavirus Media Coverage Index (MCI), Connectedness, COVID-19 pandemic crisis","lastPublishedDoi":"10.21203/rs.3.rs-75766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-75766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the dynamic return and volatility connectedness for the three most relevant agricultural commodity markets (Soft, Grain and Livestock) and the Coronavirus Media Coverage Index (MCI) extracted from RavenPack. In concrete, we apply the fresh TVP-VAR methodology proposed by Antonakakis and Gabauer (2017) during the sample period between January 22, 2020 and July 31, 2020, that is in the context of the COVID-19 pandemic crisis. Interesting results are found in this research. First, dynamic total return and volatility connectedness fluctuates over time, reaching a peak during the heart of the global pandemic crisis, initially in the dynamic total return connectedness and later in the volatility one. Second, in the dynamic connectedness TO the system, we observe significant differences between the agricultural commodity markets in the level of the return connectedness measure. However, in the dynamic volatility connectedness TO, there are very few differences between some elements of the system, highlighting the Coronavirus MCI as one of the major transmitters TO the system at some points in the sample. This Coronavirus MCI appears as the less relevant receiver FROM the system, not only in terms of dynamic return connectedness, but also in volatility. Finally, regarding the net dynamic total connectedness, the Coronavirus MCI shows the highest values in return and volatility. Next, in order of highest to lowest net dynamic return and volatility connectedness we find the Grain commodity market, then the Soft market and finally the Livestock market. This last commodity market shows a negative net dynamic connectedness throughout the entire sample period analysed, exhibiting negative peaks in return at the beginning of the pandemic’s epicentre and in volatility in the middle of the COVID-19 crisis.\u003c/p\u003e","manuscriptTitle":"Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-11 15:47:37","doi":"10.21203/rs.3.rs-75766/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":"4504128f-ba91-4fd4-90e4-be2c8b4175be","owner":[],"postedDate":"September 11th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":484980,"name":"Finance"},{"id":484981,"name":"International Economics"}],"tags":[],"updatedAt":"2022-01-31T08:18:25+00:00","versionOfRecord":{"articleIdentity":"rs-75766","link":"https://doi.org/10.1080/00036846.2021.1973949","journal":{"identity":"applied-economics","isVorOnly":true,"title":"Applied Economics"},"publishedOn":"2021-09-05 08:18:25","publishedOnDateReadable":"September 5th, 2021"},"versionCreatedAt":"2020-09-11 15:47:37","video":"","vorDoi":"10.1080/00036846.2021.1973949","vorDoiUrl":"https://doi.org/10.1080/00036846.2021.1973949","workflowStages":[]},"version":"v1","identity":"rs-75766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-75766","identity":"rs-75766","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}