actuar (version 0.9-4)

aggregateDist: Aggregate Claim Amount Distribution

Description

Compute the aggregate claim amount cumulative distribution function of a portfolio over a period using one of five methods.

Usage

aggregateDist(method = c("recursive", "convolution", "normal",
                         "npower", "simulation"),
              model.freq = NULL, model.sev = NULL, p0 = NULL,
              x.scale = 1, moments, nb.simul, ...,
              tol = 1e-06, maxit = 500, echo = FALSE)
## S3 method for class 'aggregateDist':
print(x, \dots)
## S3 method for class 'aggregateDist':
plot(x, xlim, ylab = expression(F[S](x)),
     main = "Aggregate Claim Amount Distribution",
     sub = comment(x), ...)
## S3 method for class 'aggregateDist':
summary(object, \dots)
## S3 method for class 'aggregateDist':
mean(x, \dots)

Arguments

method

method to be used

model.freq

for "recursive" method: a character string giving the name of a distribution in the $(a, b, 0)$ or $(a, b, 1)$ families of distributions. For "convolution" method: a vector of claim number probabilities. For "

model.sev

for "recursive" and "convolution" methods: a vector of claim amount probabilities. For "simulation" method: a severity simulation model (see simpf for detail

p0

arbitrary probability at zero for the frequency distribution. Creates a zero-modified or zero-truncated distribution if not NULL. Used only with "recursive" method.

x.scale

value of an amount of 1 in the severity model (monetary unit). Used only with "recursive" and "convolution" methods.

moments

vector of the true moments of the aggregate claim amount distribution; required only by the "normal" or "npower" methods.

nb.simul

number of simulations for the "simulation" method.

...

parameters of the frequency distribution for the "recursive" method; further arguments to be passed to or from other methods otherwise.

tol

the recursion in the "recursive" method stops when the cumulative distribution function is less than tol away from 1.

maxit

maximum number of recursions in the "recursive" method.

echo

logical; echo the recursions to screen in the "recursive" method.

x, object

an object of class "aggregateDist".

xlim

numeric of length 2; the $x$ limits of the plot.

ylab

label of the y axis.

main

main title.

sub

subtitle, defaulting to the calculation method.

Value

• A function of class "aggregateDist", inheriting from the "function" class when using normal and Normal Power approximations and additionally inheriting from the "ecdf" and "stepfun" classes when other methods are used.

  There are methods available to summarize (summary), represent (print), plot (plot), compute quantiles (quantile) and compute the mean (mean) of "aggregateDist" objects.

Recursive method

The frequency distribution is a member of the $(a, b, 0)$ family of discrete distributions if p0 is NULL and a member of the $(a, b, 1)$ family if p0 is specified.

model.freq must be one of "binomial", "geometric", "negative binomial", "poisson" or "logarithmic" (these can abbreviated). The parameters of the frequency distribution must be specified using names identical to the arguments of functions dbinom, dgeom, dnbinom, dpois and dnbinom, respectively. (The logarithmic distribution is a limiting case of the negative binomial distribution with size parameter equal to 0.)

model.sev is a vector of the (discretized) claim amount distribution $X$; the first element must be $f_X(0) = \Pr[X = 0]$.

Failure to obtain a cumulative distribution function less than tol away from 1 within maxit iterations is often due to a too coarse discretization of the severity distribution.

Convolution method

The cumulative distribution function (cdf) $F_S(x)$ of the aggregate claim amount of a portfolio in the collective risk model is $$F_S(x)=\sum _{n=0}^{\mathrm{\infty }}{F}_X^{\ast n}(x)p_n,$$ for $x = 0, 1, \dots$; $p_n = \Pr[N = n]$ is the frequency probability mass function and $F_X^{*n}(x)$ is the cdf of the $n$th convolution of the (discrete) claim amount random variable.

model.freq is vector $p_n$ of the number of claims probabilities; the first element must be $Pr[N = 0]$.

model.sev is vector $f_X(x)$ of the (discretized) claim amount distribution; the first element must be $f_X(0)$.

Normal and Normal Power 2 methods

The Normal approximation of a cumulative distribution function (cdf) $F(x)$ with mean $\mu$ and standard deviation $\sigma$ is $$F(x)\approx \mathrm{\Phi }\left(\frac{x-\mu }{\sigma }\right).$$

The Normal Power 2 approximation of a cumulative distribution function (cdf) $F(x)$ with mean $\mu$, standard deviation $\sigma$ and skewness $\gamma$ is $$F(x)\approx \mathrm{\Phi }(-\frac{3}{\gamma }+\sqrt{\frac{9}{{\gamma }^2}+1+\frac{6}{\gamma }\frac{x-\mu }{\sigma }}).$$ This formula is valid only for the right-hand tail of the distribution and skewness should not exceed unity.

Simulation method

This methods returns the empirical distribution function of a sample of size nb.simul of the aggregate claim amount distribution specified by model.freq and model.sev. simpf is used for the simulation of claim amounts, hence both the frequency and severity models can be mixtures of distributions.

Details

aggregateDist returns a function to compute the cumulative distribution function (cdf) of the aggregate claim amount distribution in any point.

The "recursive" method computes the cdf using the Panjer algorithm; the "convolution" method using convolutions; the "normal" method using a normal approximation; the "npower" method using the Normal Power 2 approximation; the "simulation" method using simulations. More details follow.

References

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2004), Loss Models, From Data to Decisions, Second Edition, Wiley.

Daykin, C.D., Pentik�inen, T. and Pesonen, M. (1994), Practical Risk Theory for Actuaries, Chapman & Hall.

See Also

discretize to discretize a severity distribution; mean.aggregateDist to compute the mean of the distribution; quantile.aggregateDist to compute the quantiles or the Value at Risk; CTE.aggregateDist to compute the Conditional Tail Expectation; simpf.

Examples

## Convolution method (example 6.6 of Klugman et al. (2004))
fx <- c(0, 0.15, 0.2, 0.25, 0.125, 0.075,
        0.05, 0.05, 0.05, 0.025, 0.025)
pn <- c(0.05, 0.1, 0.15, 0.2, 0.25, 0.15, 0.06, 0.03, 0.01)
Fs <- aggregateDist("convolution", model.freq = pn,
                    model.sev = fx, x.scale = 25)
summary(Fs)
c(Fs(0), diff(Fs(25 * 0:21))) # probability mass function
plot(Fs)

## Recursive method
Fs <- aggregateDist("recursive", model.freq = "poisson",
                    model.sev = fx, lambda = 3, x.scale = 25)
plot(Fs)

## Normal Power approximation
Fs <- aggregateDist("npower", moments = c(200, 200, 0.5))
Fs(210)

## Simulation method
model.freq <- expression(data = rpois(3))
model.sev <- expression(data = rgamma(100, 2))
Fs <- aggregateDist("simulation", nb.simul = 1000,
                    model.freq, model.sev)
mean(Fs)
plot(Fs)

## Evaluation of ruin probabilities using Beekman's formula with
## Exponential(1) claim severity, Poisson(1) frequency and premium rate
## c = 1.2.
fx <- discretize(pexp(x, 1), from = 0, to = 100, method = "lower")
phi0 <- 0.2/1.2
Fs <- aggregateDist(method = "recursive", model.freq = "geometric",
                    model.sev = fx, prob = phi0)
1 - Fs(400)			# approximate ruin probability
u <- 0:100
plot(u, 1 - Fs(u), type = "l", main = "Ruin probability")