Operational Risk Modelling in Insurance and Banking


The author of the presented paper is trying to develop and implement the model that can mimic the state of the art models of operational risk in insurance. It implements generalized Pareto distribution and Monte Carlo simulation and tries to mimic and construct operational risk models in insurance. At the same time, it compares lognormal, Weibull and loglogistic distribution and their application in insurance industry. It is known that operational risk models in insurance are characterized by extreme tails, therefore the following analysis should be conducted: the body of distribution should be analyzed separately from the tail of the distribution. Afterwards the convolution method can be used to put together the annual loss distribution by combining the body and tail of the distribution. Monte Carlo method of convolution is utilized. Loss frequency in operational risk in insurance and overall loss distribution based on copula function, in that manner using student-t copula and Monte Carlo method are analysed. The aforementioned approach represents another aspect of observing operational risk models in insurance. This paper introduces: 1) Tools needed for operational risk models; 2) Application of R code in operational risk modeling;3) Distributions used in operational risk models, specializing in insurance; 4) Construction of operational risk models.

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Vukovic, O. (2015) Operational Risk Modelling in Insurance and Banking. Journal of Financial Risk Management, 4, 111-123. doi: 10.4236/jfrm.2015.43010.

Conflicts of Interest

The authors declare no conflicts of interest.


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