Panjer recursion

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The Panjer recursion is an algorithm to compute the probability distribution approximation of a compound random variable S = \sum_{i=1}^N X_i\,. where both N\, and X_i\, are random variables and of special types. In more general cases the distribution of S is a compound distribution. The recursion for the special cases considered was introduced in a paper [1] by Harry Panjer (Emeritus professor, University of Waterloo[2]). It is heavily used in actuarial science (see also systemic risk).

Preliminaries[edit]

We are interested in the compound random variable S = \sum_{i=1}^N X_i\, where N\, and X_i\, fulfill the following preconditions.

Claim size distribution[edit]

We assume the X_i\, to be i.i.d. and independent of N\,. Furthermore the X_i\, have to be distributed on a lattice h \mathbb{N}_0\, with latticewidth h>0\,.

f_k = P[X_i = hk].\,

In actuarial practice, X_i\, is obtain by discretisation of the claim density function (upper, lower...).

Claim number distribution[edit]

The number of claims N is a random variable, which is said to have a "claim number distribution", and which can take values 0, 1, 2, .... etc.. For the "Panjer recursion", the probability distribution of N has to be a member of the Panjer class, otherwise known as the (a,b,0) class of distributions. This class consists of all counting random variables which fulfill the following relation:

P[N=k] = p_k= (a + \frac{b}{k}) \cdot p_{k-1},~~k \ge 1.\,

for some a and b which fulfill a+b \ge 0\,. The initial value p_0\, is determined such that \sum_{k=0}^\infty p_k = 1.\,

The Panjer recursion makes use of this iterative relationship to specify a recursive way of constructing the probability distribution of S. In the following W_N(x)\, denotes the probability generating function of N: for this see the table in (a,b,0) class of distributions.

In the case of claim number is known, please note the De Pril algorithm. This algorithm is suitable to compute the sum distribution of n discrete random variables.[3]

Recursion[edit]

The algorithm now gives a recursion to compute the g_k =P[S = hk] \,.

The starting value is g_0 = W_N(f_0)\, with the special cases

g_0=p_0\cdot \exp(f_0 b)\text{ if }a = 0,\,

and

g_0=\frac{p_0}{(1-f_0a)^{1+b/a}}\text{ for }a \ne 0,\,

and proceed with

g_k=\frac{1}{1-f_0a}\sum_{j=1}^k \left( a+\frac{b\cdot j}{k} \right) \cdot f_j \cdot g_{k-j}.\,

Example[edit]

The following example shows the approximated density of \scriptstyle S \,=\, \sum_{i=1}^N X_i where \scriptstyle N\, \sim\, \text{NegBin}(3.5,0.3)\, and \scriptstyle X \,\sim \,\text{Frechet}(1.7,1) with lattice width h = 0.04. (See Fréchet distribution.)

Expba07.jpg

References[edit]

  1. ^ Panjer, Harry H. (1981). "Recursive evaluation of a family of compound distributions." (PDF). ASTIN Bulletin (International Actuarial Association) 12 (1): 22–26. 
  2. ^ CV, actuaries.org; Staff page, math.uwaterloo.ca
  3. ^ De Pril, N. (1988). "Improved approximations for the aggregate claims distribution of a life insurance portfolio". Scandinavian Actuarial Journal 1988: 61. doi:10.1080/03461238.1988.10413837.  edit

External links[edit]