Financial density selection
J. Miguel Marin and
Genaro Sucarrat
The European Journal of Finance, 2015, vol. 21, issue 13-14, 1195-1213
Abstract:
We 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 DGP 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.
Date: 2015
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/1351847X.2012.706906 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: Financial Density Selection (2012) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:eurjfi:v:21:y:2015:i:13-14:p:1195-1213
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/REJF20
DOI: 10.1080/1351847X.2012.706906
Access Statistics for this article
The European Journal of Finance is currently edited by Chris Adcock
More articles in The European Journal of Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().