  In this paper, we model dependence between operational risks by allowing risk
profiles to evolve stochastically in time and to be dependent. This allows for
a flexible correlation structure where the dependence between frequencies of
different risk categories and between severities of different risk categories
as well as within risk categories can be modeled. The model is estimated using
Bayesian inference methodology, allowing for combination of internal data,
external data and expert opinion in the estimation procedure. We use a
specialized Markov chain Monte Carlo simulation methodology known as Slice
sampling to obtain samples from the resulting posterior distribution and
estimate the model parameters.
