Transformation kernel estimation of insurance claim cost distributions

Catalina Bolancé, Montserrat Guillén and Jens Perch Nielsen
Additional contact information
Catalina Bolancé: University of Barcelona, Department Econometrics, RFA-IREA
Montserrat Guillén: University of Barcelona, Department Econometrics, RFA-IREA
Jens Perch Nielsen: City University London, Cass Business School

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2010, pp 43-51 from Springer

Abstract: Abstract A transformation kernel density estimator that is suitable for heavy-tailed distributions is discussed. Using a truncated beta transformation, the choice of the bandwidth parameter becomes straightforward. An application to insurance data and the calculation of the value-at-risk are presented.

Keywords: non-parametric statistics; actuarial loss models; extreme value theory (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
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:spr:sprchp:978-88-470-1481-7_5

Ordering information: This item can be ordered from
http://www.springer.com/9788847014817

DOI: 10.1007/978-88-470-1481-7_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().