Description: This paper discusses a general model for calculating life contingencies including formulation of the model and net reserves and contingency reserves.

Description: In this paper we discuss some properties of the nth stop-loss order and their application in risk premium principles. We give a necessary condition and a sufficient condition for nth stop-loss orders. They are convenient tools to construct risk pairs with nth stop-loss orders. We show that exponential premium principles can diferentiate between losses more finely than the net premium principles under some conditions.

Description: This paper considers the Bayesian analysis of the generalized Poisson distribution GPD and the generalized Poisson regression GPR model. Markov chain Monte Carlo MCMC methods and random variety generation strategies such as adaptive rejection sampling ARS for log-concave densities and adaptive rejection Metropolis sampling ARMS are discussed and then utilized in order to advance the Bayesian analysis of these generalized Poisson models.

Description: The concept of transformed distributions is generalized in this paper. First the concepts of net premium intensity, loaded premium intensity and load generators arc introduced. Then transformed distributions are identified with premium intensity and hence the loaded premium is calculated from transformed distributions. Finally it is shown that this method of premimn calculation is arbitrage free and it incorporates the strengths of the utility approach.

Description: A European call option pricing model similar to the Black-Scholes equation [1] is derived. Like the Black-Scholes equation, the model is based upon an assumption of a lognormal distribution of the price of a risky, non-dividend-paying security at a given point in the future. However, unlike Black-Scholes, the market's risk preference is recognized through a parameter that permits refinement of the model to agree either with experience or with future advances in the underlying theory.

Description: This paper addresses the stochastic modeling for managing asset liability process. We start with developing a jump-diffusion process for evaluating of the liabilities of the insurance company in general. We then formulate the ALM process into a stochastic control problem. With this approach, we present a Bel|man-Dreyfus Fundamental type formula for ALM process in terms of the solution of a system of algebraic equations and partial differential equations.

Description: Applications of Lagrangian distributions to modelling claim frequency data in an insurance portfolio is a relatively new concept. The major difficulty is that the generating functions of these distributions cannot be expressed in terms of elementary functions. Also deriving moments and/or cumulants is somewhat tedious. This article illustrates how to use Maple effectively to overcome these difficulties.

Description: The classical approach to life contingencies has treated actuarial functions as fixed quantities. In point of fact, these functions really represent the mean or expected values of random variables which are functions at the length of time to death of a life aged x. In this paper, we will present some examples of this approach. Talk given at the thirteenth annual Actuarial Research Conference held at Ball State University, Muncie, Indiana, September 1, 1978.

Description: This is the abstract of the paper Minimum Quadratic Distance Estimators for the Zeta Parametric Family. In this paper, we propose a new estimator, based on quadratic distance, asymptotically fully efficient for parameter values greater than 2 and highly efficient for smaller values, but computationally more appealing than the maximum likelihood estimator we compare its asymptotic variance with that of the moment estimator and an estimator proposed by Seal, based on the ratio of the observed frequencies of the first two classes. We also discuss a goodness-of-fit test and a test to discriminate between the zeta distribution and the Poisson distribution truncated at 0.

Description: This is a summary of the presentation given during the ARC Conference. Its purpose was to give a brief introduction to subexponential behavior and to show that the family of subexponential distributions provides ideal characteristics to model insurance risks. Although the literature related to this topic accumulated quickly during the last years, a textbook explaining the applications of extreme value theory with intuition as well as insight was still to be written. Embrechts et al, {I 997} satisfies this need and most of the results presented here can be found in this new classic in the actuarial literature.