  Insurance risks data typically exhibit skewed behaviour. In this paper, we
propose a Bayesian approach to capture the main features of these datasets.
This work extends the methodology introduced in Villa and Walker (2014a) by
considering an extra parameter which captures the skewness of the data. In
particular, a skewed Student-t distribution is considered. Two datasets are
analysed: the Danish fire losses and the US indemnity loss. The analysis is
carried with an objective Bayesian approach. For the discrete parameter
representing the number of the degrees of freedom, we adopt a novel prior
recently introduced in Villa and Walker (2014b).
