A Dynamical Approach to Operational Risk Measurement
We propose a dynamical model for the estimation of Operational Risk in banking institutions. Operational Risk is the risk that a financial loss occurs as the result of failed processes. Examples of operational losses are the ones generated by internal frauds, human errors or 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 to follow the whole time evolution of the losses and to take into account different-time correlations among the processes.
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- R. G. Cowell & R. J. Verrall & Y. K. Yoon, 2007. "Modeling Operational Risk With Bayesian Networks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(4), pages 795-827.
- Kartik Anand & Reimer K\"uhn, 2006. "Phase Transitions in Operational Risk," Papers physics/0609130, arXiv.org, revised Dec 2006.
- Kühn, Reimer & Neu, Peter, 2003. "Functional correlation approach to operational risk in banking organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 650-666.
- Cornalba, Chiara & Giudici, Paolo, 2004. "Statistical models for operational risk management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 166-172.
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