Description: The modern valuation manual steps up the need for actuaries to know and apply statistics. If your recall of common actuarial statistical techniques is more than one standard deviation below the mean, this session will provide the lift you need.

Description: This 1997 presentation from the 32nd Actuarial Research Conference develops a stochastic generator for the generation of scenarios of the S&P 500 index, dividend yield, consumer price index, and U.S. Treasury yields. The authors first create a set of 'stylized facts' for these series. They estimate statistical models for these series. These in-sample statistical models are themselves not suitable for generation of scenarios for decision making, but instead are additional 'stylized facts' that assist in model development. The 'best' statistical model according to standard statistical model selection criteria can easily lead to a model that is highly unsuitable for generation of scenarios for decision making. The authors develop a stochastic generator that is suitable for decision making under uncertainty.

Description: In this article, the author describes how to get data into and out of R.

Description: This paper analyzes the U. S. Treasury monthly interest rates from 1953 to 1992, and the daily rates from 1981 to 1992 for various forms of chaotic behavior. The primary analysis was in determining the Burst Exponent by the use of Rescaled Range Analysis. This paper also discusses other possible uses of Fractional Brownian Motion in Finance Theory, Macroeconomics, and Actuarial Science. From Actuarial Research Clearing House 1994 VOL. 1.

Description: In this presentation, the authors develop a stochastic generator for the generation of scenarios of the S & P 500 index, dividend yield, consumer price index, and U.S. Treasury yields. They first create a set of 'stylized facts' for these series. These in-sample statistical models are themselves not suitable for generation of scenarios for decision making, but instead are additional stylized facts that assist in model development. The 'best' statistical model according to standard statistical model selection criteria can easily lead to a model that is highly unsuitable for generation of scenarios for decision making. The authors develop a stochastic generator that is suitable for decision making under uncertainty. From Actuarial Research Clearing House 1997, Vol. 1.

Description: Discusses the graphics capabilities of the computer language R.

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

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

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.