Please enable JavaScript.
Coggle requires JavaScript to display documents.
Verrall: predictive distribution for reserves which incorporate expert…
Verrall
: predictive distribution for reserves which incorporate expert opinion
Introduction
One important property of Bayesian methods that makes them suitable for use with a stochastic reserving model is that they allow us to incorporate expert knowledge in a natural way
The basic idea behind MCMC methods is to simulate the posterior distribution by breaking the simulation process down into a number of simulations that are as easy to carry out as possible.
Instead of trying to simulate all the parameters at once, MCMC methods use the conditional distribution of each parameter, given all the others. In this way, the simulation is reduced to a univariate distribution, which is much easier to deal with.
In general, when selecting prior distributions, the variance assigned to the distribution dictates how the model will fit to the data
Higher variances give less credibility to the prior mean set, meaning that the distribution will ultimately fit to whatever the data indicates
A lower variance assigns more weight to the prior mean set
Key definitions
C
incremental losses
D
cumulative losses
Phi
dispertion parameter
x
row factor = expected losses
y
columne factor % unpaid
landa
age to age factor
Models
ODP models
The models give the same reserve estimates as the chain ladder technique (as long as the row and column sums of the incremental claims triangle are positive)
ODNB models
Normal distribution
can deal with the problem of negative incremental claims
MSE of the prediction
the expected square difference between the actual outcome and the predicted value
the
standard error
is the square root of the estimation variance.
The
prediction error
is concerned with the variability of a forecast, taking account of uncertainty in parameter estimation and also of the inherent variability in the data being forecast