Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest
Abstract
Nowadays financial institutions due to regulation and internal motivations care more intensively
on their risks. Besides previously dominating market and credit risk new trend is to handle operational risk systematically. Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. First we show the basic features of operational risk and its modelling and regulatory approaches, and after we will analyse
operational risk in an own developed simulation model framework. Our approach is based on the
analysis of latent risk process instead of manifest risk process, which widely popular in risk
literature. In our model the latent risk process is a stochastic risk process, so called Ornstein-
Uhlenbeck process, which is a mean reversion process. In the model framework we define catastrophe as breach of a critical barrier by the process. We analyse the distributions of catastrophe frequency, severity and first time to hit, not only for single process, but for dual process as well. Based on our first results we could not falsify the Poisson feature of frequency, and long tail feature of severity. Distribution of “first time to hit” requires more sophisticated analysis. At the end of paper we examine advantages of simulation based forecasting, and finally we concluding with the possible, further research directions to be done in the future
