Jim Gustafsson, presenterar sitt examensarbete

A semiparametric approach to loss distribution modelling with
application to operational risk assessment


The subject of this thesis is a one-method-fits-all approach to
quantify and predict future losses in insurance. This method is based
on a semiparametric estimator which is corrected by some nonparametric
smoothing techniques. A number of alternative kernel functions are
considered for removing boundary bias, resulting from transforming
data with a parametric function to bounded support. We analyse the
crucial point of bandwidth selection in nonparametric statistics and
discuss three different bandwidth selection methods. We also present a
practical application based on operational risk data. Operational risk
itself is defined as the risk of loss arising from inadequate or
failed internal processes, people and systems or from external events.