Description: The Cauchy integral formula is applied to derive various results on divided differences.

Description: In Volume LXXV of the Proceedings there appeared a paper entitled Risk Loads for Insurers by Feldblum. Confidence intervals for the betas in TABLE 4 [Profit Margins and Their Standard Deviations by Line of Business] of this paper can be generated using the assumption of underlying normal distributions. However, bootstrapping operational or not for interval estimates does not require the normality assumption. The method of operational bootstrapping does not use random numbers. The method for the 4-dimensional case, used to generate the interval estimates in the current paper, is a natural generalization of the bivariate case. I use operational bootstrapping to generate interval estimates for Betas shown in the TABLE 5.

Description: The theory underlying the Moving Weighted Average graduation method is restated in the language of linear algebra which provides for an enrichment of the family of formulas. A comparison of the results of applying six members of the family to 100 simulated data sets is provided.

Description: In this continuation of our July 2013 article we explore an algorithm for evaluating Hidden Markov Models in detail. A preferred method in speech recognition technology, Hidden Markov Models are a promising technique when dealing with data you suspect may be driven by a change in a unobservable state. The example in the article is provided in an Excel workbook for the reader to follow along with.

Description: By the use of resampling and extreme value statistics we will develop a method to reduce the time and costs of testing insurance company insolvency. Most ruin models require assumptions about the surplus distribution and/or assumptions about the claims count and severity distributions. The actuary may not be comfortable in making these ruin assumptions. Instead, he or she may generate such distributions by running extensive computer simulations of corporate models, I introduce a method to reduce the number of simulations necessary' to estimate the 1 and 0.1 percentiles of the surplus distribution. This approach 'cuts off' the tail at approximately the tenth percentile of the sample distribution and generates new tails for the distribution by resampling. Averages of the new order statistics approximate the I and 0.1 percentiles.

Description: By necessity, attributions of financial processes require approximations. Tools used for approximations include Aumann-Shapley, time step, step-through, average step-through, partial derivative, and average partial derivative. Significantly non-linear paths can cause problems during attribution. Time (theta) and market factors must be handled concurrently. Typically contractual factors can be handled separately. Average Step-Through, Aumann-Shapley and Aumann-Shapley-3 points perform the best given the same time period. Attribution should be done with fairly small time steps as opposed to using a lot of points in the Aumann-Shapley calculation, but using Aumann-Shapley-3 points as a last step has some advantages.

Description: The purpose of this paper is to introduce a practical method for interpolation and smoothing and give a number of numerical examples using the method. The method is a variation of the Whittaker-Henderson method. Discussions of this paper are included.

Description: The purpose of this paper is to illustrate the use of a new approximation to determine contingency reserves against adverse mortality experience for a portfolio of life annuity contracts.

Description: Sooner or later a casualty actuary is confronted by the question, Given a history of paid loss amounts by calendar year, what should reserves be? Often, it is not possible to accurately gather and analyze additional data within the time constraints for the reserving decision. The algebraic reserving method presented in this paper offers one approach to rapidly addressing this problem. The paper consists of four sections - General Considerations, Formulas, Examples, and Conclusion.

Description: This paper presents a few formulaic examples displaying the advantages of geometric methods over arithmetic methods and provides some theorems proving the increased accuracy geometric methods can provide for interpolation or extrapolation