Description: In the present paper, we propose and investigate a numerical method of computing the probability distribution of S. Inversion of the Laplace transform moment generating function is shown to be a better approach than the existing recursive method for find the distribution of sum of several independent random variables. In the continuous case, in many situations, numerical inversion needs to be used to compute the probability distribution function of the sum.

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: The amount of 1 [i.e., the amount that $ 1 is worth after 1 year] for an account earning a nominal interest r compounded n times annually is Pn[r,t], where t = [t1, . . . ,tn]. Note that Pn[r,t] is a polynomial in r of degree n. Now suppose that the n compounding periods have lengths T1, . . . ,Tn chosen at random. What can we say about Pn[r , T] , where T=[T1, . . . , Tn]? This depends of course on what is meant by choosing random lengths and we will consider two different natural possibilities that give rise to different answers to this question.

Description: Given a financial transaction we are often interested in the number of internal rates of return [yield rates] and their location. From an actuarial point of view, the fundamental question is to identify those transactions with a single yield rate, or a single rate in some interval such as the positive reals. In this paper we are concerned with this counting problem. We survey various results concerning this problem, and provide a unifying approach together with some new insights.

Description: The purpose of this paper is to discuss computer application, the management of an insurance enterprise in an automobile insurance marketplace wherein a number of insurers compete for marketshare and profit. The paper begins with a description of the computer program next, a cursory description of the model is given then, the assumptions of the model are discussed next, potential future modifications are discussed and, finally, a prognosis is given.

Description: Random sum models with compound distributions are used extensively in modeling of insurance risks. Unfortunately, the compound distributions themselves are awkward to evaluate. Consequently, various numerical and analytical approximative techniques have been used. In this talk we derive various upper and lower bounds on the tails of compound distributions. Some notions in reliability theory are used.

Description: This paper introduces a new direction for evaluating numerically survival probabilities pointed out by H. Seal in his book ‘Survival Probabilities: The goal of Risk Theory’. Some of special cases can be considered as a weighted Poisson process with an infinitely divisible mixing distribution. This property is well known for the negative binomial case [gamma mixing distribution].

Description: Demography is a very exciting subject. With a simple mathematical model, students can project a country's population forward 25, 59, 75 even 100 years. This paper will focus on six ideas from demography. They all can be useful to an actuary, yet none of them would be considered at any length if the actuary didn’t study demography.

Description: After reading the Panjer-Bellhouse paper in the Proceedings of the Ball State University Conference, I wondered whether their technique could be used when a realistic investment process was assumed. I believe that such an extension is necessary if the theory is to have any practical application I have had little success in trying to do this. I have decided to present some thoughts and to invite others with different perspectives to bring them to fruition.

Description: The conventional way to analyze grouped observations on a single variable is to construct histograms or, sometimes, fit a normal distribution. We extend this latter approach by allowing for transformations to near normality.