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

Description Usage Arguments Value Author(s) See Also Examples

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

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

`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

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

Marius Pfeuffer

sla

### 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% Value-at-Risk
VaR(mc_out,.95)