About the Series
CAS monographs are authoritative, peer-reviewed, in-depth works focusing on important topics within property and casualty actuarial practice. The inaugural monograph, Stochastic Loss Reserving Using Bayesian MCMC Models, was published in January 2015.
The CAS Monograph Series initiative fulfills the goal of creating an important addition to the existing body of CAS literature, with each monograph enabling the comprehensive treatment of a single subject.
The monographs represent just one way that the CAS provides its members with access to relevant information, research and resources that they can apply directly on the job to advance in their careers.
CAS Monograph No. 1 –Stochastic Loss Reserving Using Bayesian MCMC Models, by Glenn Meyers, FCAS, MAAA, CERA
In this monograph, Glenn Meyers introduces a novel way of testing the predictive power of two loss reserving methodologies. Using a database created by the CAS that consists of hundreds of loss development triangles with outcomes, the volume begins by first testing the performance of the Mack model on incurred data and the Bootstrap Overdispersed Poisson model on paid data. As the emergence of Bayesian MCMC models has provided actuaries with an unprecedented flexibility in stochastic model development, the monograph then identifies some Bayesian MCMC (Markov Chain Monte Carlo) models that improve the performance over the above models.
CAS Monograph No. 2 –Distributions for Actuaries, by David Bahnemann
In Distributions for Actuaries, David Bahnemannbrings together two important elements of actuarial practice: an academic presentation of parametric distributions and the application of these distributions in the actuarial paradigm. The focus is on the use of parametric distributions fitted to empirical claim data to solve standard actuarial problems, such as creation of increased limit factors, pricing of deductibles, and evaluating the effect of aggregate limits.
CAS Monograph No. 3 – Stochastic Loss Reserving Using Generalized Linear Models by Greg Taylor and Gráinne McGuire
In this monograph, authors Greg Taylor and Gráinne McGuire discuss generalized linear models (GLM) for loss reserving, beginning with strong emphasis on the chain ladder. The chain ladder is formulated in a GLM context, as is the statistical distribution of the loss reserve. This structure is then used to test the need for departure from the chain ladder model and to consider natural extensions of the chain ladder model that lend themselves to the GLM framework.
CAS Monograph No. 4 – Using the ODP Bootstrap Model: A Practitioner’s Guide by Mark Shapland
In this monograph, Using the ODP Bootstrap Model: A Practitioner’s Guide, the fourth volume of the new CAS Monograph Series, author Mark R. Shapland, a Fellow of the CAS, discusses the practical issues and solutions for dealing with the limitations of ODP bootstrapping models, including practical considerations for selecting the best assumptions and the best model for individual situations. The focus is on the “practical” and the paper illustrates the diagnostic tools that an actuary needs to assess whether a model is working well.
CAS Monograph No. 5 – Generalized Linear Models for Insurance Rating by Mark Goldburd, Anand Khare, and Dan Tevet
In this monograph, Generalized Linear Models for Insurance Rating, the fifth volume of the new CAS Monograph Series, authors Mark Goldburd, Anand Khare, and Dan Tevet have written a comprehensive guide to creating an insurance rating plan using generalized linear models (GLMs), with an emphasis on practical application. While many textbooks and papers exist to explain GLMs, this monograph serves as a “one-stop shop” for the actuary seeking to understand the GLM model-building process from start to finish. The monograph has been adopted on the syllabus for Exam 8. (Notice of corrections made)
CAS Monograph No. 6 – A Machine-Learning Approach to Parameter Estimation by Jim Kunce and Som Chatterjee
In this monograph, A Machine-Learning Approach to Parameter Estimation, the sixth volume of the CAS Monograph Series, CAS Fellows Jim Kunce and Som Chatterjee address the use of machine-learning techniques to solve insurance problems. Their model can use any regression-based machine-learning algorithm to analyze the nonlinear relationships between the parameters of statistical distributions and features that relate to a specific problem. Unlike traditional stratification and segmentation, the authors’ machine-learning approach to parameter estimation (MLAPE) learns the underlying parameter groups from the data and uses validation to ensure appropriate predictive power.
A CAS monograph is a comprehensive, peer-reviewed work on a single aspect of property casualty actuarial science that is primarily educational in nature. Papers are welcomed from anyone regardless of their profession, education, or geographic location. Submissions will be evaluated based upon the merits of the paper and not authorship. Submission guidelines are available, or can be obtained by contacting Donna Royston, CAS Publications Production Coordinator at email@example.com. For a synopsis of the key steps in the creation of a monograph, please read How a CAS Monograph is Created.