Description: Stochastic approximation is a powerful tool for sequential estimation of zero points of a function. In this paper, this methodology is defined and is shown to be related to a broad class of credibility formulae derived for the Exponential Dispersion Family. The authors further consider a Location Dispersion Family which is rich enough and for which no simple credibility formula exists. For this case, a Generalized Sequential Credibility Formula is suggested and an optimal stepwise gain sequence is derived.

Description: The subject of this paper is a mathematical model for insurance company risks formed by a linear combination of four stochastic processes recognizing claim patterns, investment performance assumptions, operating expenses and lapse expenses. A discussion of the paper follows. From Transactions of Society of Actuaries 1978, Vol. 30.

Description: This paper discusses stochastic treatments for insured Long Term Disability [LTD] benefits under a group insurance policy.

Description: In this paper, a mathematical model is constructed to study the deviations of claims, investment performance, operating expenses, and lapse expenses as random stochastic processes. Discussions of the paper submitted by 2 readers follows

Description: This paper introduces the symbolic valuation, a stochastic valuation which allows flexible interest rate and cash flow assumptions, and provides an illustration which values a simple SPDA product. From Actuarial Research Clearing House 1992, Vol. 1.

Description: This paper studies the tail probability of discounted aggregate claims in a continuous-time renewal model. For the case that the common claim-size distribution is subexponential, the authors obtain an asymptotic formula, which holds uniformly for all time horizons within a finite interval. Then, with some additional mild assumptions on the distributions of the claim sizes and inter-arrival times, the authors further prove that this formula holds uniformly for all time horizons.

Description: Design and future maintenance of an asset portfolio backing a new line of business is critical for proper asset and liability management for that business. Most portfolio optimization methods utilize linear or quadratic programming and require the user to specify the asset and liability attributes and cash flows into the program. The programmer must also supply an objective function to allow the program to find the optimal asset mix for the associated liabilities. Two problems with this approach are that the liability cash flows are fairly static and that one must frequently rebalance the portfolio. An alternative approach would be to develop a corporate model of both the assets and liabilities and incorporate various economic scenarios as input into the model. Asset strategies would be measured against a specific objective function that is calculated by the corporate model. Unfortunately, the majority of maximization algorithms available are very time consuming, and obtaining a reasonable portfolio mix becomes impractical. This paper overcomes this difficulty by using a very rapid optimization method from the chemical engineering profession called the Floppy Triangle. The paper includes an example of the use of this process to determine an optimal portfolio. It also discusses modifications of the algorithm that is required when the shorting of assets is not permitted.

Description: With this paper, a new theory of immunization is introduced in which the approach is multivariate, and the goal is stochastic in the sense of minimizing stochastic risk. Discussions of this paper are included.

Description: This review summarizes the key features of a stochastic model that works with non-life insurance claim incurral, accrual and reporting. More specifically it provides an overview of the claim model components, classical parameter estimators, test statistics, and forecast point estimates and error variances. From Actuarial Research Clearing House 1991, VOL. 1.

Description: The Financial Reporting and Product Development Sections, along with the Committee on Life Insurance Research are pleased to make the following report available. Prepared by Bruce Rosner of Ernst & Young, the report summarizes the findings of a stochastic modeling efficiency study. With the help of twelve life insurance and annuity companies, the study tested six techniques for reducing the number of model points or scenarios required to achieve a given level of precision in stochastic actuarial modeling.