| Library: | times |
| See also: | simNHPP simNHPPRP simNHPPRPedfRT simNHPPRPmeanloss simNHPPRPmeanlossRT simNHPPRPRT |
| Quantlet: | simNHPPRPedf | |
| Description: | generates risk process driven by the non-homogeneous Poisson process with claims generated from empirical distribution function. |
| Usage: | y = simNHPPRPedf(u,theta,lambda,parlambda,val,T,N) | |
| Input: | ||
| u | scalar, initial capital | |
| theta | scalar, relative safety loading | |
| lambda | scalar, intensity function, sine function (lambda=0), linear function (lambda=1), or sine square function (lambda=2) | |
| parlambda | n x 1 vector, parameters of the intensity function lambda (n=2 for lambda=1, n=3 otherwise) | |
| val | p x 1 vector, empirical losses | |
| T | scalar, time horizon | |
| N | scalar, number of trajectories | |
| Output: | ||
| y | 2*max+2 x N x 2 array, generated process - max is the maximum number of jumps for all generated trajectories | |
library("xplore")
library("times")
library("plot")
randomize2(101)
randomize(101)
y1=simNHPPRPedf(10,0.2,0,#(1,1,0),rndexp(500,1,1),5,1)
y1=reduce(y1)
d1 = createdisplay(1,1)
adddata(d1, 1, 1,setmask(y1,"line","medium","red", "style",1))
y2=simNHPPRPedf(10,0.2,0,#(1,1,0),rndexp(1500,1,1),5,1)
y2=reduce(y2)
adddata(d1, 1, 1,setmask(y2,"line","medium","blue", "style",1))
Show two trajectories of risk process driven by the non-homogeneous Poisson process with claims generated from empirical distribution function.