Murphy Diagrams: Forecast Evaluation of Expected Shortfall
Motivated by the Basel 3 regulations, recent studies have considered joint forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring functions can be used to evaluate forecast performance in this context. However, little intuitive or empirical guidance is currently available, whic...
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creator | Ziegel, Johanna F Krüger, Fabian Jordan, Alexander Fasciati, Fernando |
description | Motivated by the Basel 3 regulations, recent studies have considered joint
forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring
functions can be used to evaluate forecast performance in this context.
However, little intuitive or empirical guidance is currently available, which
renders the choice of scoring function awkward in practice. We therefore
develop graphical checks (Murphy diagrams) of whether one forecast method
dominates another under a relevant class of scoring functions, and propose an
associated hypothesis test. We illustrate these tools with simulation examples
and an empirical analysis of S&P 500 and DAX returns. |
doi_str_mv | 10.48550/arxiv.1705.04537 |
format | Article |
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forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring
functions can be used to evaluate forecast performance in this context.
However, little intuitive or empirical guidance is currently available, which
renders the choice of scoring function awkward in practice. We therefore
develop graphical checks (Murphy diagrams) of whether one forecast method
dominates another under a relevant class of scoring functions, and propose an
associated hypothesis test. We illustrate these tools with simulation examples
and an empirical analysis of S&P 500 and DAX returns.</description><identifier>DOI: 10.48550/arxiv.1705.04537</identifier><language>eng</language><subject>Quantitative Finance - Risk Management ; Statistics - Applications</subject><creationdate>2017-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1705.04537$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1705.04537$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ziegel, Johanna F</creatorcontrib><creatorcontrib>Krüger, Fabian</creatorcontrib><creatorcontrib>Jordan, Alexander</creatorcontrib><creatorcontrib>Fasciati, Fernando</creatorcontrib><title>Murphy Diagrams: Forecast Evaluation of Expected Shortfall</title><description>Motivated by the Basel 3 regulations, recent studies have considered joint
forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring
functions can be used to evaluate forecast performance in this context.
However, little intuitive or empirical guidance is currently available, which
renders the choice of scoring function awkward in practice. We therefore
develop graphical checks (Murphy diagrams) of whether one forecast method
dominates another under a relevant class of scoring functions, and propose an
associated hypothesis test. We illustrate these tools with simulation examples
and an empirical analysis of S&P 500 and DAX returns.</description><subject>Quantitative Finance - Risk Management</subject><subject>Statistics - Applications</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj71uwjAYAL0wVMADdMIvkNR_n-2wIRpoJSoG2KMvjg2RAomcgODtobTTbac7Qt45S5UFYB8Yb_U15YZByhRI80bmP5fYHe_0s8ZDxFM_p6s2eof9QPMrNhcc6vZM20DzW-fd4Cu6O7ZxCNg0EzJ6ovfTf47JfpXvl1_JZrv-Xi42CWpjEgGgFFQ2BKGV9TJTMggmnCsrH0AYi6UvPReaa8yktiwwBESRgbbcKSbHZPanfcUXXaxPGO_F70TxmpAPCJRBEA</recordid><startdate>20170512</startdate><enddate>20170512</enddate><creator>Ziegel, Johanna F</creator><creator>Krüger, Fabian</creator><creator>Jordan, Alexander</creator><creator>Fasciati, Fernando</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20170512</creationdate><title>Murphy Diagrams: Forecast Evaluation of Expected Shortfall</title><author>Ziegel, Johanna F ; Krüger, Fabian ; Jordan, Alexander ; Fasciati, Fernando</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-255445d8ff2648e3943f202ccbdef5278abebe12616a93680f0a5aa295681c403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Quantitative Finance - Risk Management</topic><topic>Statistics - Applications</topic><toplevel>online_resources</toplevel><creatorcontrib>Ziegel, Johanna F</creatorcontrib><creatorcontrib>Krüger, Fabian</creatorcontrib><creatorcontrib>Jordan, Alexander</creatorcontrib><creatorcontrib>Fasciati, Fernando</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ziegel, Johanna F</au><au>Krüger, Fabian</au><au>Jordan, Alexander</au><au>Fasciati, Fernando</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Murphy Diagrams: Forecast Evaluation of Expected Shortfall</atitle><date>2017-05-12</date><risdate>2017</risdate><abstract>Motivated by the Basel 3 regulations, recent studies have considered joint
forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring
functions can be used to evaluate forecast performance in this context.
However, little intuitive or empirical guidance is currently available, which
renders the choice of scoring function awkward in practice. We therefore
develop graphical checks (Murphy diagrams) of whether one forecast method
dominates another under a relevant class of scoring functions, and propose an
associated hypothesis test. We illustrate these tools with simulation examples
and an empirical analysis of S&P 500 and DAX returns.</abstract><doi>10.48550/arxiv.1705.04537</doi><oa>free_for_read</oa></addata></record> |
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subjects | Quantitative Finance - Risk Management Statistics - Applications |
title | Murphy Diagrams: Forecast Evaluation of Expected Shortfall |
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