Description: “Many commentators have suggested that firms need to do stress tests to examine their vulnerability to adverse situations that are not within the data set used to parameterize their risk models. We suggest the adop¬tion of a terminology to describe stress tests and also a meth¬odology that can be adopted by any risk model user to test and communicate a test of the stability of model results.”

Description: This article, with some humor, describes a movement known as “econophysics” of physicists modeling economic systems, including capital markets, using techniques and concepts developed in studying the out-of-equilibrium dynamics of complex systems. A version of this article appeared in The Actuary UK in August 2004. Reprinted with permission.

Description: The article discusses the investment models of The Mutual Pension Insurance Company Ilmarinen, one of the companies managing the statutory employment pension in Finland. The parts are: financial time series data management, time series analysis programs, investment simulation models and portfolio optimization models.

Description: Announcement of an upcoming 2003 Stochastic Modeling Symposium.

Description: This article discusses facts and issues related to the selection of a stock’s price distribution.

Description: This article focuses on one fundamental modeling assumption — the choice of a benchmark rate or risk-free rate. The author looks at this bond market assumption as a case study of how one needs to monitor fundamental market changes for their impact on assumptions.

Description: In this article about interest rate models, the author develops an empirical description of the term “interest rate regime,” and looks at the relationship between changes in yield curves and moving to a new interest rate regime.

Description: The article warns against treating the results of a stochastic model with more deference than other actuarial analysis, and describes some hidden risks involved with choice of model and calibration.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: What is going on with the exchange rate, anyway? In 2002, $1 U.S. bought $C 1.60, and now in May 2005 you only get $C 1.25—that’s 20 percent less! Where’s it going next? One way to answer that question is with a time series model.