Estimating large losses in insurance analytics and operational risk using the g-and-h distribution

In this paper, we study the estimation of parameters for g-and-h distributions. These distributions find applications in modeling highly skewed and fat-tailed data, like extreme losses banking insurance sector. We first introduce two methods: a numerical maximum likelihood technique, an indirect inference approach with bootstrap weighting scheme. realistic simulation study, show that is computationally more efficient provides better estimates than method case features data. Empirical illustrations on operational illustrate these findings.