Local Transformation Kernel Density Estimation of Loss Distributions

J. Gustafsson, M. Hagmann, J.P. Nielsen and Olivier Scaillet
Additional contact information
J. Gustafsson: Codan Insurance and University of Copenhagen, Copenhagen, Denmark
M. Hagmann: University of Geneva and Concordia Advisors, London, United Kingdom
J.P. Nielsen: Festina Lente and University of Copenhagen, Copenhagen, Denmark

No 06-32, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We develop a tailor made semiparametric asymmetric kernel density estimator for the estimation of actuarial loss distributions. The estimator is obtained by transforming the data with the generalized Champernowne distribution initially fitted to the data. Then the density of the transformed data is estimated by use of local asymmetric kernel methods to obtain superior estimation properties in the tails. We find in a vast simulation study that the proposed semiparametric estimation procedure performs well relative to alternative estimators. An application to operational loss data illustrates the proposed method.

Keywords: Actuarial loss models; Transformation; Champernowne distribution; asymmetric kernels; local likelihood estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2006-11, Revised 2007-06
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Local Transformation Kernel Density Estimation of Loss Distributions (2009)
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