A dynamical approach to operational risk measurement

We propose a dynamical model for the estimation of operational risk in bank- ing institutions. Operational risk is the risk that a financial loss occurs as the result of failed processes. Examples of operational losses are losses generated by internal fraud, human error and failed transactions. In order to encompass the most heterogeneous set of processes, in our approach the losses of each process are generated by the interplay among random noise, interactions with other processes and the efforts the bank makes to avoid losses. We show how some relevant parameters of the model can be estimated from a database of historical operational losses, validate the estimation procedure and test the forecasting power of the model. Some advantages of our approach over the traditional statistical techniques are that it allows us to follow the whole time evolution of the losses and to take into account different-time correlations among the processes.