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: 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: The April 2016 Issue of The Modeling Platform

Description: A summary of Modeling Section-sponsored sessions at the 2016 Life/Annuity Symposium, covering model risk management, model governance, and model efficiency.

Description: Feature article discussing numerical short cuts, rules of thumb, estimators, modeling tips, etc. The featured problem is fitting the intermediate points for a real-world stochastic model of interest rates.

Description: The Risk Management Task Force polled the Risks & Rewards readers and asked the following questions related to Monte Carlo models: 1 How many scenarios do you run? 2 How did you determine the number? and 3 What confidence interval does your result have? The results are summarized in this article.

Description: A study on stochastic simulation literature as it applies to a health actuary.

Description: This session from the 1995 SOA Boston Meeting covers a general overview of basic interest rate models, the meaning of an arbitrage free model, the considerations in choosing an appropriate interest rate scenario generator, the uses, alternative approaches, the double mean reverting process model - DMRP, and how good the results are in terms of historical rates. From the Record of the Society of Actuaries, Vol. 21, No. 4b.

Description: Discussion of how to obtain tight upper and lower bounds on E[hX] for a given function h, and a random variable X with three known moments.

Description: This article looks at stochastic techniques actuaries can use to quantify the non-market risks in insurance products and how stochastic techniques can be used in the evaluation of reinsurance arrangements.