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dc.contributor.authorParaschiv, Florentina
dc.contributor.authorReese, Stine Marie
dc.contributor.authorSkjelstad, Margrethe Ringkjøb
dc.date.accessioned2020-08-26T09:07:09Z
dc.date.available2020-08-26T09:07:09Z
dc.date.created2019-09-23T17:04:36Z
dc.date.issued2020
dc.identifier.citationComputational Management Science. 2020, .en_US
dc.identifier.issn1619-697X
dc.identifier.urihttps://hdl.handle.net/11250/2674187
dc.description.abstractIn this article, we construct a portfolio of commodity futures which mimics the Dow Jones Commodity Index and perform an extensive stress testing exercise with a focus on hybrid scenarios. The increased volume of investments in commodities as financial instruments over the last decades underline the importance of a more thorough framework for stress testing of related portfolios. Our study is the first to show comparatively the marginal impact of the model choice for portfolio components versus the marginal role of tail dependence on the portfolio profit and loss in stress testing exercises. We model the distribution of returns of portfolio components with an asymmetric AR-GARCH model combined with Extreme Value Theory for extreme tails, and employ multivariate copula functions to model the time-varying joint dependence structure. Our study reveals that indeed, for a realistic stress test, a special attention should be given to the tail risk in individual commodity returns as well as to tail correlations. We also draw conclusions about parameter risk persistent in stress testing exercises. Finally yet importantly, in line with Basel IIIb, the study highlights the importance of using forward-looking hybrid and hypothetical scenarios over historical scenarios.en_US
dc.language.isoengen_US
dc.publisherSpringer Verlagen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titlePortfolio Stress Testing Applied to Commodity Futuresen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber38en_US
dc.source.journalComputational Management Scienceen_US
dc.identifier.doi10.1007/s10287-020-00370-9
dc.identifier.cristin1727986
dc.description.localcodeOpen Access This article is licensed under a Creative Commons Attribution 4.0 International Licenseen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.fulltextpostprint
cristin.fulltextoriginal
cristin.qualitycode1


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