Computational Actuarial Science with R

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).

Introduction Arthur Charpentier and Rob Kaas Methodology Standard Statistical Inference Christophe Dutang Bayesian Philosophy Arthur Charpentier and Ben Escoto Statistical Learning Arthur Charpentier and Stéphane Tufféry Spatial Analysis Renato Assunção, Marcelo Azevedo Costa, Marcos Oliveira Prates, and Luís Gustavo Silva e Silva Reinsurance and Extremal Events Eric Gilleland and Mathieu Ribatet Life Insurance Life Contingencies Giorgio Spedicato Prospective Life Tables Heather Booth, Rob J. Hyndman, and Leonie Tickle Prospective Mortality Tables and Portfolio Experience Julien Tomas and Frédéric Planchet Survival Analysis Frédéric Planchet and Pierre-E. Thérond Finance Stock Prices and Time Series Yohan Chalabi and Diethelm Würtz Yield Curves and Interest Rates Models Sergio S. Guirreri Portfolio Allocation Yohan Chalabi and Diethelm Würtz Non-Life Insurance General Insurance Pricing Jean-Philippe Boucher and Arthur Charpentier Longitudinal Data and Experience Rating Katrien Antonio, Peng Shi, and Frank van Berkum Claims Reserving and IBNR Markus Gesmann Bibliography Index R Command Index

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

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).