Local Transformation Kernel Density Estimation of Loss Distributions

We develop a tailor made semiparametric asymmetric kernel density estimator for the es- timation of actuarial loss distributions. The estimator is obtained by transforming the data with the generalized Champernowne distribution initially fitted to the data. Then the den- sity 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 pro- posed semiparametric estimation procedure performs well relative to alternative estimators. An application to operational loss data illustrates the proposed method