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.