Description: Article describing new and interesting computer programs.

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

Description: Letter from the new chairman of the Technology Section Council introducing the new council members.

Description: Feature article discussing the latest computer programs.

Description: Introduces new co-editor Eddie Smith and includes letters to the editor.

Description: Discusses the benefits of using the Instapaper application to manage and retrieve web articles.

Description: Discusses the section’s LinkedIn group and the contents of the January issue of the newsletter.

Description: This is the abstract of a paper that complements the existing research on optimal reinsurance by proposing another model for the determination of the optimal reinsurance design.

Description: Many insurers preparing market consistent financial statements encounter the issue of developing volatility assumptions for equity options and guarantees that are longer than any observable market data. This presentation develops a generalization of the classical Black Scholes option pricing model that is consistent with the European CRO Forum's framework for valuing non-hedgeable risk. The presentation argues that, with reasonable parameter choices, the model fits observed volatility surfaces well enough that it can be used to estimate implied volatilities where no market data exists.

Description: This paper addresses the fuzziness in regression models. The goal is to present a test procedure to explicitly examine whether an independent variable has a clear functional relationship with the dependent variable in a specific regression model, or whether their relationship is fuzzy. To this end, the authors interpret the spread of the regression coefficients as a statistic measuring the fuzziness of the relationship between the corresponding independent variable and the dependent variable. They then derive test distributions based on the null hypothesis that such spreads could have been obtained with data generated by a classical regression model with random errors. The analysis is presented in conceptual rather than technical terms.