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A Hands-On Approach to Understanding and Using Actuarial Models
Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes.
After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance.
Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).
- Sales Rank: #912480 in Books
- Published on: 2016-10-29
- Original language: English
- Dimensions: 10.00" h x 7.00" w x 1.50" l,
- Binding: Paperback
- 656 pages
Review
"… the main objective of the book is that the reader gets interested in the topic and plays with the presented models and R codes in an active way. I have experienced that this goal can be easily reached for a large audience of readers because the presentation of the various arguments encourages an active learning of the concepts ‘without being burdened by the theory.’"
―International Statistical Review, 83, 2015
"… worthwhile reading and can be recommended to anyone who is interested in the computational aspects of actuarial science. The book contains many detailed worked examples, with R code fully integrated into the text. … the book provides information and code that readers with any quantitative background can gain something from. It will naturally appeal to actuaries of all calibers, but it has a much wider audience of quantitative analysts using R for statistical modeling and data analysis in various fields. There are also good reasons to recommend this book to any science library."
―Journal of the Royal Statistical Society, Series A, 2015
About the Author
Arthur Charpentier is a professor of actuarial science at the University of Québec at Montréal. He is a fellow of the French Institute of Actuaries and holds a PhD in applied mathematics from K.U. Leuven. Dr. Charpentier is the co-author of two textbooks on mathematical models of nonlife insurance and has published several articles in peer-reviewed journals. He is also the editor of the blog freakonometrics.hypotheses.org
Most helpful customer reviews
4 of 4 people found the following review helpful.
It is a great book. Comprehensive and with good information
By S. Wang
It is a great book. Comprehensive and with good information.
One thing I found difficult is the dataset. After looking around I found a download site for installing the dataset package, but still could not find dataset for chapter 14, for example. I found one github site for source code, but that is incomplete. The book would be more approachable if its supplemental materials are made easily available.
1 of 1 people found the following review helpful.
Great Book
By Colin Thompson
I enjoyed reading through this book. The material was well explained and code was clear. The only reason I didn't give it 5 stars is it doesn't appear to have answers to the questions in each section. Some sections have answers posted on the web by the sections co-authors.
I do modeling work in operational risk and this book is perfect for working with R. Looking forward to Dr. Charpentier's next work.
0 of 0 people found the following review helpful.
This is an excellent cook book
By Robert L McPherson
This is an excellent cook book, of sorts, with many recipes for implementing a wide variety of methods in R, to solve a very wide variety of analytical problems in insurance.
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