Description: The cloud inspires awe and fear in equal parts these days. There is a need to learn more, but there’s much information to learn and it may be difficult to know what to learn next. In this session, we’ll carve off a small corner of the cloud and make it your own. Setting up a Data Science Virtual Machine on Digital Ocean will be demonstrated. It’s the fastest way to get a data science development environment setup for your own use. Many of the topics and ideas covered will apply to almost all other cloud offerings. What use cases Digital Ocean is good for, and what use cases you should look elsewhere for, will also be discussed.

Description: This article discussed modeling the P&C reinsurance market using an Agent Based Model to provide insights into the market. The characteristics of this marketplace provide credibility to this type of modeling. Specifically, the P&C reinsurance market has few major players, very little product differentiation, and low barriers to entry. In addition established competitors are volume-constrained due to prior business and capacity decisions made based on estimated product costs and prices.

Description: This article discusses the pending book entitled 'Dynamic Risk Modeling Handbook.' The proposed scope and outline of the book is reviewed. This is a revision of the 'DFA Handbook' which was published in 1995.

Description: Chairperson talks about the activities of the section.

Description: This abstract describes a paper that considers an alternative way of estimating distribution parameters for nonnegative random variables.

Description: This paper demonstrates the calculation of the moments of the distribution of aggregate life insurance claims from seriatim inforce data, assuming that each record represents an independent Bernoulli trial with its own known probability of a claim, q. Continuous distributions matching the first three or four moments are identified.

Description: This paper develops credibility using a longitudinal data framework. In a longitudinal data framework, one might encounter data from a cross-section of risk classes towns with a history of insurance claims available for each risk class. For the marginal claims distributions, we use generalized linear models, an extension of linear regression that also encompasses Weibull and Gamma regressions. Copulas are used to model the dependencies over time specifically, this paper is the first to propose using a t-copula in the context of generalized linear models. The t-copula is the copula associated with the multivariate t-distribution like univariate t-distributions, it seems especially suitable for empirical work. Moreover, we show that the t-copula gives rise to easily computable predictive distributions that we use to generate credibility predictors. Like Bayesian methods, our copula credibility prediction methods allow us to provide an entire distribution of predicted claims, not just a point prediction.

Description: Presentation from the 41st Actuarial Research Conference held on August 10-12, 2006 in Montreal, Canada. Fuzzy set theory is incorporated into times series analysis, giving fuzzy parameter estimates of models such as ARMA and GARCH. Examples are given, including returns on a stock and option valuation.

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: When fitting a model to any data, there is some uncertainty about which model is best. Green [1995] quantifies this uncertainty through the Reversible Jump Markov Chain Monte Carlo [RJMCMC] method. When using the RJMCMC method in a regime-switching situation, the chain determines the optimal number of regimes by jumping between various possibilities. This method gives each model its posterior probability of being the best. After an overview of the methodology, the authors apply it to various datasets and discuss the applications in modern actuarial science.