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dc.contributor.authorMarin, J. Miguel
dc.contributor.authorSucarrat, Genaro
dc.date.accessioned2015-10-05T11:34:44Z
dc.date.available2015-10-05T11:34:44Z
dc.date.issued2015
dc.identifier.citationThe European Journal of Finance, 21(2015)13-14:1195-1213nb_NO
dc.identifier.issn1351-847x
dc.identifier.issn1466-4364
dc.identifier.urihttp://hdl.handle.net/11250/2349733
dc.descriptionThis is the manuscript to the article first published in Munich Personal RePEc Archive https://mpra.ub.uni-muenchen.de/66839/nb_NO
dc.description.abstractWe propose and study simple but flexible methods for density selection of skewed versions of the two most popular density classes in finance, the exponential power distribution and the t distribution. For the first type of method, which simply consists of selecting a density by means of an information criterion, the Schwarz criterion stands out since it performs well across density categories, and in particular when the Data Generating Process is normal. For the second type of method, General-to-Specific density selection, the simulations suggest that it can improve the recovery rate in predictable ways by changing the significance level. This is useful because it enables us to increase (reduce) the recovery rate of non-normal densities by increasing (reducing) the significance level, if one wishes to do so. The third type of method is a generalisation of the second type, such that it can be applied across an arbitrary number of density classes, nested or non-nested. Finally, the methods are illustrated in an empirical application.nb_NO
dc.language.isoengnb_NO
dc.publisherTaylor & Francisnb_NO
dc.titleFinancial density selectionnb_NO
dc.typeJournal articlenb_NO
dc.source.journalThe European Journal of Financenb_NO
dc.identifier.doi10.1080/1351847X.2012.706906
dc.description.localcode1, Forsidenb_NO


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