Local Transformation Kernel Density Estimation of Loss Distributions
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.
|Date of creation:||Nov 2006|
|Date of revision:||Jun 2007|
|Contact details of provider:|| Web page: http://www.SwissFinanceInstitute.ch|
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