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A Dynamical Model for Forecasting Operational Losses

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  • Marco Bardoscia
  • Roberto Bellotti

Abstract

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 internal organizational 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 $\simeq 10^{-3}$.

Suggested Citation

  • Marco Bardoscia & Roberto Bellotti, 2010. "A Dynamical Model for Forecasting Operational Losses," Papers 1007.0026, arXiv.org, revised Feb 2012.
    • Handle: RePEc:arx:papers:1007.0026
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    1. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169, September.
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    Cited by:

    1. Marco Bardoscia & Roberto Bellotti, 2012. "A Dynamical Approach to Operational Risk Measurement," Papers 1202.2532, arXiv.org.
    2. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.

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