OpVaR (version 1.0.5)

mcSim: Monte Carlo Simulation from opriskmodel objects for total loss estimation

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

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

See Also

sla

Examples

# 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<!-- % Value-at-Risk -->
VaR(mc_out,.95)
# }