# mcSim: Monte Carlo Simulation from opriskmodel objects for total... In OpVaR: Statistical Methods for Modeling Operational Risk

## Description

Function for conducting Monte Carlo Simulation of complete opriskmodel objects (list of cells with (1) frequency model, (2) severity model and (3) dependencymodel)

## Usage

 ```1 2``` ```mcSim(opriskmodel, n_sim, verbose=TRUE) VaR(mc_out, alpha) ```

## Arguments

 `opriskmodel` an opriskmodel object `n_sim` number of simulations `mc_out` Monte Carlo simulation output `alpha` significance level for quantile/value-at-risk `verbose` verbose mode

## Value

A mcsim object, which can be further processed by the VaR function to estimate empirical quantiles as value-at-risk measure

## Author(s)

Marius Pfeuffer

`sla`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```## Not run: ### Load Example Data Set data(lossdat) ### Estimation of Complete Risk Model opriskmodel1=list() for(i in 1:length(lossdat)){ opriskmodel1[[i]]=list() opriskmodel1[[i]]\$freqdist=fitFreqdist(lossdat[[i]],"pois") opriskmodel1[[i]]\$sevdist=fitPlain(lossdat[[i]],"lnorm") } ### Cell 1: Gumbel Copula, Cell 2: Independence, Cell 3: Frank Copula, Cell 4: Independence opriskmodel1[[1]]\$dependency=fitDependency(lossdat[[1]],6) opriskmodel1[[3]]\$dependency=fitDependency(lossdat[[3]],4) ### Monte Carlo Simulation mc_out=mcSim(opriskmodel1,100) ### Evaluation of 95 VaR(mc_out,.95) sla(opriskmodel1,.95) ### Monte Carlo Simulation mc_out=mcSim(opriskmodel1,100) ### Evaluation of 95 VaR(mc_out,.95) ## End(Not run) ```