A novel dynamical model for the study of operational risk in banks  is proposed. The equation of motion takes into account the interactions among different bank's processes, the spontaneous generation of losses via a noise term and the efforts made by the banks to avoid their occurrence. A scheme for the estimation of some parameters of the model is illustrated, so that it can be tailored on the URLanizational structure of a specific bank . We focus on the case in which there are no causal loops in the matrix of couplings and exploit the exact solution to estimate also the parameters of the noise. The scheme for the estimation of the parameters is proved to be consistent and the model is shown to exhibit a remarkable capability in forecasting future cumulative losses .
A novel dynamical model for the study of operational risk in banks and suitable for the calculation of the Value at Risk (VaR) is proposed. The equation of motion takes into account the interactions among different bank's processes, the spontaneous generation of losses via a noise term and the efforts made by the bank to avoid their occurrence. Since the model is very general, it can be tailored on the URLanizational structure of a specific bank by estimating some of its parameters from historical operational losses. The model is exactly solved in the case in which there are no causal loops in the matrix of couplings and it is shown how the solution can be exploited to estimate also the parameters of the noise. The forecasting power of the model is investigated by using a fraction f of simulated data to estimate the parameters, showing that for f = 0.75 the VaR can be forecast with an error \simeq 10^{-3 .
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and suitable for the calculation of the Value at Risk (VaR)
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banks
bank
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A scheme for the estimation of some parameters of the model is illustrated, so that
Since the model is very general,
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. We focus on
by estimating some of its parameters from historical operational losses. The model is exactly solved in
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exploit the exact solution
it is shown how the solution can be exploited
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scheme for the estimation of the parameters is proved to be consistent and the model is shown to exhibit a remarkable capability in forecasting future cumulative losses
forecasting power of the model is investigated by using a fraction f of simulated data to estimate the parameters, showing that for f = 0.75 the VaR can be forecast with an error \simeq 10^{-3