Usage: |
y = INSDeVylderfin(u, T, lambda, theta, distrib, dparameters)
|
Input: |
| u | scalar, n x 1 vector or m x n matrix, initial capital for risk process
|
| T | scalar or p x 1 vector, time horizon for risk process, vector allowed when u is not a matrix
|
| 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, loggamma, Pareto, Burr or truncPareto.
|
| dparameters | list of scalars, parameters of the following distributions: exponential, gamma, Weibull, lognormal, loggamma, Pareto, Burr or truncPareto
list of n x 1 vectors of parameters of "mixofexps" distribution, the first vector are parameters for the exponential distributions and the second one are the weights of mixing
|
Output: |
| y | scalar, p x 1 or n x 1 vector (size of T or u) or p x n matrix or m x n matrix, ruin probability in finite time horizon T
given by finite De Vylder approximation. |
- Example:
library("insurance")
library("xplore")
distrib = "gamma"
dparameters = list(0.2,0.7)
u = #(1:10)
T = 100
lambda = 5
theta = 0.3
y = INSDeVylderfin(u, T, lambda, theta, distrib, dparameters)
y
- Result:
Contents of y
[ 1,] 0.56608
[ 2,] 0.43804
[ 3,] 0.33894
[ 4,] 0.26223
[ 5,] 0.20287
[ 6,] 0.15693
[ 7,] 0.12138
[ 8,] 0.093877
[ 9,] 0.072593
[10,] 0.056127
- Example:
library("insurance")
distrib = "gamma"
dparameters = list(0.2,0.7)
u = #(1:5)
T = #(1:10)
lambda = 5
theta = 0.3
y = INSDeVylderfin(u, T, lambda, theta, distrib, dparameters)
y
- Result:
Contents of y
[ 1,] 0.24021 0.12582 0.06489 0.03302 0.01661
[ 2,] 0.33569 0.19876 0.11541 0.06587 0.03703
[ 3,] 0.38864 0.24579 0.15255 0.09309 0.05594
[ 4,] 0.42286 0.27873 0.18061 0.11520 0.07242
[ 5,] 0.44697 0.30314 0.20247 0.13330 0.08659
[ 6,] 0.46494 0.32197 0.21993 0.14829 0.09875
[ 7,] 0.47886 0.33693 0.23418 0.16086 0.10924
[ 8,] 0.48997 0.34910 0.24601 0.17151 0.11833
[ 9,] 0.49903 0.35918 0.25596 0.18063 0.12626
[10,] 0.50656 0.36765 0.26444 0.18851 0.13321