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: An insurance company starts with an initial surplus, collects premium, pays claims to policyholders and pays dividends to stockholders. What remains is the following year’s surplus. The process continues. This paper describes how to calculate the distribution of surplus for any given year. With these distributions, one can make the calculations of premiums and dividends.

Description: This article investigates the simplicity of the Balducci hypothesis, and compares the fractional-age death probability given by three widely used assumptions: uniform distribution of deaths, the Balducci hypothesis, and constant force of mortality [1, 2].

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 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.

Description: This is the fourth paper on n-year roll forward reserve financing of public benefit systems, especially Old-Age, Survivors and disability Insurance OASDI, prepared by University of Michigan faculty and students. As the papers have developed, some ideas have been simplified, while others have become more complex. It is concluded that the theoretical model requires further attention. This research can be extended to expand our knowledge of OASDI financing and the applicability of n-year roll forward reserve financing.

Description: This paper thoroughly describes risk premium calculation based on an insurance portfolio consisting of policies risks insured over a finite time interval.

Description: This is the abstract of the paper 'Pricing of Credit Derivatives'. This talk will give an overview of current pricing techniques. We first show how to build a credit curve. A credit curve gives the instantaneous default probability of a party at any time in the future. It is as important to credit derivative pricing as the yield curve is to fixed income derivative pricing.

Description: In a series papers by Willmot and Lin, both exponential and non-exponential bounds for the tail probability of various compound distributions have been derived. In Wilmot, it was suggested that non-exponential bounds for the ruin probability were difficult to obtain using martingale arguments. In this note, we show how to use martingale inequalities to obtain some upper bounds for the ruin probability.

Description: Traditional mean-variance method does not take skewness of the random rate of return into consideration. It only considers minimum variance for a given acceptable rate of return, which limited the probability of obtaining higher rate of return. If the skewness of return for the portfolio is incorporated into model of traditional approach, then the resulting non-linear programing model is not computational efficient, which certainly is complicated. On the other hand, possibilistic programming provides more efficient techniques to solve the imprecision nature of rate of return and also preserves the original linear model.