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