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: 2011 Enterprise Risk Management Symposium, Chicago. This is the abstract of the entitled paper.

Description: Feature article that introduces readers to the R computer language.

Description: These are the slides that go with the paper 'Mortality Modeling using Projection Pursuit Regression.' In that paper, the authors develop sixteen separate mortality models based on the current SOA mortality data being used for the preferred life study. The models are constructed using projection pursuit regression. The sixteen models arise from all combinations of sex, smoker status, select and ultimate status, and per life vs. per dollar mortality rates. In general the models confirm the following four facts: 1 Mortality rates increase as attained age increases. 2 Mortality rates increase as duration increases. 3 Mortality rates decrease as Face Amount Bands increase. 4. Mortality rates decrease if preferred underwriting is present.

Description: This is an image of an example table from a MS Access database.

Description: Discusses using the tseries package in the R computer language for developing historical series of data.

Description: These are Charts 1-4 from a presentation entitled 'Stochastic Immunization' From a session at a meeting of the Society of Actuaries held in San Diego, CA, June 22-23, 2000

Description: This article recaps session 19 "Estimating Mortality Risk Using Predictive Modeling".

Description: 2011 Enterprise Risk Management Symposium, Chicago. This presentation discusses how real world Grey Swan scenarios are excellent choices for stress and resiliency testing. In addition, an example is provided of a set of Grey Swan scenarios which correspond to the RBC C-3, Phase II.

Description: These are Charts 5-8 from a presentation entitled 'Stochastic Immunization'. From a session at a meeting of the Society of Actuaries held in San Diego, CA, June 22-23, 2000