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) from CRAN.

Table of Contents

Chapter 1. Introduction

Part I: Methodology
Chapter 2. Standard Statistical Inference
Chapter 3. Bayesian Philosophy
Chapter 4. Statistical Learning
Chapter 5. Spatial Analysis
Chapter 6. Reinsurance and Extremal Events

Part II: Life Insurance
Chapter 7. Life Contingencies
Chapter 8. Prospective Life Tables
Chapter 9. Prospective Mortality Tables and Portfolio Experience
Chapter 10. Survival Analysis

Part III: Finance
Chapter 11. Stock Prices and Time Series
Chapter 12. Yield Curves and Interest Rates Models
Chapter 13. Portfolio Allocation

Part IV: Non-Life Insurance
Chapter 14. General Insurance Pricing
Chapter 15. Longitudinal Data and Experience Rating
Chapter 16. Claims Reserving and IBNR