Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions

Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one‐step‐ahead conditio...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of money, credit and banking credit and banking, 2024-08, Vol.56 (5), p.1099-1127
Hauptverfasser:	CARRIERO, ANDREA, CLARK, TODD E., MARCELLINO, MASSIMILIANO
Format:	Artikel
Sprache:	eng
Schlagworte:	asymmetries Bayesian analysis downside risk Forecasting Inflation Specification Structural analysis Unemployment Volatility
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	1127
container_issue	5
container_start_page	1099
container_title	Journal of money, credit and banking
container_volume	56
creator	CARRIERO, ANDREA CLARK, TODD E. MARCELLINO, MASSIMILIANO
description	Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one‐step‐ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, BVAR models perform comparably to QR for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.
doi_str_mv	10.1111/jmcb.13121
format	Article
fullrecord	<record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3086248795</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3086248795</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3911-97f90edaf73fb48d3f9e84dc25d2e69d9739a005240fbb54c69d4334f79b54fc3</originalsourceid><addsrcrecordid>eNp9kMtKAzEUhoMoWKsbnyDgTpia21yybId6KS2CVLchk0lqajtTkxnK7HwEn9EnMXUEd57FOZzDx3_4fwAuMRrhUDfrrSpGmGKCj8AAxzSLWIKTYzBAiJCIZCk_BWferxFCPGZ4AGa53DWts9UKLqRy9dfH51TVVb21Ci6l3cAn69883NvmFU5kp72VFXzRqqkdHLeh65XT3tu68ufgxMiN1xe_cwieb6fL_D6aP9495ON5pCjHOOKp4UiX0qTUFCwrqeE6Y6UicUl0wkueUi4RiglDpihipsKNUcpMysNmFB2Cq1535-r3VvtGrOvWVeGloChLCAsu40Bd91Qw5b3TRuyc3UrXCYzEIStxyEr8ZBVg2MM6eLf-D-WIU5Tg7KCHe2RvN7r7R0zMFvmkl_0GCw129Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3086248795</pqid></control><display><type>article</type><title>Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>CARRIERO, ANDREA ; CLARK, TODD E. ; MARCELLINO, MASSIMILIANO</creator><creatorcontrib>CARRIERO, ANDREA ; CLARK, TODD E. ; MARCELLINO, MASSIMILIANO</creatorcontrib><description>Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one‐step‐ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, BVAR models perform comparably to QR for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.</description><identifier>ISSN: 0022-2879</identifier><identifier>EISSN: 1538-4616</identifier><identifier>DOI: 10.1111/jmcb.13121</identifier><language>eng</language><publisher>Columbus: Ohio State University Press</publisher><subject>asymmetries ; Bayesian analysis ; downside risk ; Forecasting ; Inflation ; Specification ; Structural analysis ; Unemployment ; Volatility</subject><ispartof>Journal of money, credit and banking, 2024-08, Vol.56 (5), p.1099-1127</ispartof><rights>2023 The Ohio State University.</rights><rights>2024 The Ohio State University.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3911-97f90edaf73fb48d3f9e84dc25d2e69d9739a005240fbb54c69d4334f79b54fc3</citedby><cites>FETCH-LOGICAL-c3911-97f90edaf73fb48d3f9e84dc25d2e69d9739a005240fbb54c69d4334f79b54fc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fjmcb.13121$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fjmcb.13121$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids></links><search><creatorcontrib>CARRIERO, ANDREA</creatorcontrib><creatorcontrib>CLARK, TODD E.</creatorcontrib><creatorcontrib>MARCELLINO, MASSIMILIANO</creatorcontrib><title>Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions</title><title>Journal of money, credit and banking</title><description>Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one‐step‐ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, BVAR models perform comparably to QR for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.</description><subject>asymmetries</subject><subject>Bayesian analysis</subject><subject>downside risk</subject><subject>Forecasting</subject><subject>Inflation</subject><subject>Specification</subject><subject>Structural analysis</subject><subject>Unemployment</subject><subject>Volatility</subject><issn>0022-2879</issn><issn>1538-4616</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKAzEUhoMoWKsbnyDgTpia21yybId6KS2CVLchk0lqajtTkxnK7HwEn9EnMXUEd57FOZzDx3_4fwAuMRrhUDfrrSpGmGKCj8AAxzSLWIKTYzBAiJCIZCk_BWferxFCPGZ4AGa53DWts9UKLqRy9dfH51TVVb21Ci6l3cAn69883NvmFU5kp72VFXzRqqkdHLeh65XT3tu68ufgxMiN1xe_cwieb6fL_D6aP9495ON5pCjHOOKp4UiX0qTUFCwrqeE6Y6UicUl0wkueUi4RiglDpihipsKNUcpMysNmFB2Cq1535-r3VvtGrOvWVeGloChLCAsu40Bd91Qw5b3TRuyc3UrXCYzEIStxyEr8ZBVg2MM6eLf-D-WIU5Tg7KCHe2RvN7r7R0zMFvmkl_0GCw129Q</recordid><startdate>202408</startdate><enddate>202408</enddate><creator>CARRIERO, ANDREA</creator><creator>CLARK, TODD E.</creator><creator>MARCELLINO, MASSIMILIANO</creator><general>Ohio State University Press</general><scope>OQ6</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>202408</creationdate><title>Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions</title><author>CARRIERO, ANDREA ; CLARK, TODD E. ; MARCELLINO, MASSIMILIANO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3911-97f90edaf73fb48d3f9e84dc25d2e69d9739a005240fbb54c69d4334f79b54fc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>asymmetries</topic><topic>Bayesian analysis</topic><topic>downside risk</topic><topic>Forecasting</topic><topic>Inflation</topic><topic>Specification</topic><topic>Structural analysis</topic><topic>Unemployment</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>CARRIERO, ANDREA</creatorcontrib><creatorcontrib>CLARK, TODD E.</creatorcontrib><creatorcontrib>MARCELLINO, MASSIMILIANO</creatorcontrib><collection>ECONIS</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of money, credit and banking</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>CARRIERO, ANDREA</au><au>CLARK, TODD E.</au><au>MARCELLINO, MASSIMILIANO</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions</atitle><jtitle>Journal of money, credit and banking</jtitle><date>2024-08</date><risdate>2024</risdate><volume>56</volume><issue>5</issue><spage>1099</spage><epage>1127</epage><pages>1099-1127</pages><issn>0022-2879</issn><eissn>1538-4616</eissn><abstract>Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one‐step‐ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, BVAR models perform comparably to QR for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.</abstract><cop>Columbus</cop><pub>Ohio State University Press</pub><doi>10.1111/jmcb.13121</doi><tpages>29</tpages></addata></record>
fulltext	fulltext
identifier	ISSN: 0022-2879
ispartof	Journal of money, credit and banking, 2024-08, Vol.56 (5), p.1099-1127
issn	0022-2879 1538-4616
language	eng
recordid	cdi_proquest_journals_3086248795
source	Wiley Online Library Journals Frontfile Complete
subjects	asymmetries Bayesian analysis downside risk Forecasting Inflation Specification Structural analysis Unemployment Volatility
title	Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T23%3A48%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Capturing%20Macro%E2%80%90Economic%20Tail%20Risks%20with%20Bayesian%20Vector%20Autoregressions&rft.jtitle=Journal%20of%20money,%20credit%20and%20banking&rft.au=CARRIERO,%20ANDREA&rft.date=2024-08&rft.volume=56&rft.issue=5&rft.spage=1099&rft.epage=1127&rft.pages=1099-1127&rft.issn=0022-2879&rft.eissn=1538-4616&rft_id=info:doi/10.1111/jmcb.13121&rft_dat=%3Cproquest_cross%3E3086248795%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3086248795&rft_id=info:pmid/&rfr_iscdi=true