XploRe Help : INSruinMC

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library:	insurance
See also:	INSruinMC INSexactexpfin simHPPRP

Quantlet:		INSruinMC
Description:		produces the ruin probability in finite time horizon for insurance collective risk model by Monte Carlo simulations of risk process.

Reference(s):

P. Cizek, W. Haerdle, R. Weron (2004): "Statistical Tools for Finance and Insurance"

Usage:	`P = INSruinMC(u, T, lambda, theta, distrib, dparameters, N, niter)`
Input:
	`u`	scalar or n x 1 vector, initial capital for risk process
	`T`	scalar or p x 1 vector, time horizon for risk process
	`lambda`	scalar, intensity of loss arrivals driven by Poisson process
	`theta`	scalar, security loading in insurance collective risk model
	`distrib`	string, name of distribution of claims, either: exponential, gamma, mixofexps, Weibull, lognormal, Pareto or Burr.
	`dparameters`	list, composed of scalars, (parameters of the following distributions: exponential, gamma, Weibull, lognormal, loggamma, Pareto, Burr or truncPareto) or of n x 1 vectors (parameters of "mixofexps" distribution, the first vector are parameters for the exponential distributions and the second one are the weights of mixing).
	`N`	scalar, number of simulated trajectories from risk process
	`niter`	scalar, number of iterations of the procedure to be repeated
Output:
	`P`	scalar (if both T and u are scalars), p x 1 vector (if only T is a scalar), n x 1 vector (if only u is a scalar) or p x n matrix (else), ruin probability in finite time horizon T.

Note:

In the first example, the loss distribution is chosen to be Pareto, intesity of loss arrivals 5, safety loading 0.3, initial capital a vector from 1 to 10 and time horizon 100.

Note:

In the second example, the initial capital is a vector of values from 1 to 5, time horizon a vector of values 10, 20, 30.

Example:

library("insurance") library("xplore") library("distribs") randomize2(101) randomize(101) distrib = "Pareto" dparameters = list(1.2,0.7) u = #(1:4) T = 100 lambda = 5 theta = 0.3 N = 100 niter = 10 y = INSruinMC(u, T, lambda, theta, distrib, dparameters, N, niter) y

Result:

Contents of y
[1,]     0.53
[2,]    0.492
[3,]    0.468
[4,]     0.45

Example:

library("insurance") library("xplore") library("distribs") randomize2(101) randomize(101) distrib = "Pareto" dparameters = list(1.2,0.7) u = #(1:5) T = #(1:3)*10 lambda = 5 theta = 0.3 N = 200 niter = 10 y = INSruinMC(u, T, lambda, theta, distrib, dparameters, N, niter) y

Result:

Contents of y
[1,]   0.4155    0.376   0.3475   0.3325    0.319
[2,]   0.4515   0.4135   0.3865    0.372   0.3585
[3,]    0.468   0.4325   0.4055   0.3905    0.377

Author: P. Mista, 20041118 license MD*Tech