This paper attempts to give an overview of the pricing of risks in a pure exchange economy, where trade takes place at time zero and where uncertainty is revealed at time one. An economic equilibrium model under uncertainty is formulated, where conditions characterizing a Pareto optimal exchange equilibrium are derived. We present two sets of sufficient conditions for the existence of an equilibrium, and demonstrate how equilibria can be characterized through several examples. Uniqueness of equilibrium is also discussed. Special attention is given to the principal components that the premiums in a reinsurance market must depend upon. We also apply the general theory to the risk exchange problem between a policyholder and an insurer, and in particular we compute market premiums of the resulting optimal contracts.

It is emphasized throughout how the formulation of a competitive equilibrium, rather than merely a general risk exchange formulation, is of particular interest in deriving a well-defined and unique set of equilibrium premiums in an insurance market. The theory is put into a framework which is fruitful for extensions beyond the one-period case.

A distribution-free formula for the standard error of chain ladder reserve estimates is derived and compared to the results of some parametric methods using a numerical example.

Based on recurrence equation theory and relative error (rather than absolute error) analysis, the concept and criterion for the stability of a recurrence equation are clarified. A family of recursions, called congruent recursions, is proved to be strongly stable in evaluating its non-negative solutions. A type of strongly unstable recursion is identified. The recursive formula discussed by Panjer (1981) is proved to be strongly stable in evaluating the compound Poisson and the compound Negative Binomial (including Geometric) distributions. For the compound Binomial distribution, the recursion is shown to be unstable. A simple method to cope with this instability is proposed. Many other recursions are reviewed. Illustrative numerical examples are given.

In this paper we derive formulae for finite time survival probabilities when the aggregate claims process is a Gamma process. We illustrate how a compound Poisson process can be approximated by a Gamma process and by a process defined as a translated Gamma process. We also show how survival probabilities for a compound Poisson process can be approximated by those for a Gamma process or a translated Gamma process.

Weighted mortality rates are commonly used in actuarial work, but the inter-relationship between the weights used and the underlying mortality rates seems not to have been widely investigated.

Calculation of the ratio of weighted mortality rates to conventional mortality rates provides a simple means for an insurance company to track changes in the underlying mortality of its portfolio over time, and acts as an early warning system for possible deterioration of underwriting results. Asymptotic distributions are found for this ratio, and for the mortality rates themselves. It is suggested that insurance companies commence to gather data for the calculation of this ratio for the insurance sector as a whole, for the main annuity and assurance classes.

The incidence of risk under a credit insurance policy depends on the original term of the policy and the policy duration at which the incidence of risk is considered. Section 3 of the paper describes the procedure used to fit a bivariate function to this incidence. Section 4 gives the numerical detail of this model. Section 5 makes a comparison of the model with the data from which it was developed. Section 6 adds some general comments.

In an earlier note the present author deduced bounds for the approximation error of stop loss premiums when the aggregate claims distribution is calculated by a method introduced by Bertram. From the error bounds of the stop loss premiums we deduced bounds for the approximation error of the cumulative distribution and the discrete density of the aggregate claims. In the present note we shall improve the bounds for the cumulative distribution and the discrete density.