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Computational Actuarial Science with R

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Actuarial Models
actuarial science R programming guide
algorithms involved in actuarial computations
Bayesian inference techniques
Black Litterman Model
Category=KCH
Category=KF
Category=PBT
Category=PBW
Chain Ladder Method
code
computational aspects of actuarial science
computational facets of life insurance
Cumulative Distribution Function
Deviance Residuals
distribution
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
FALSE FALSE
FALSE FALSE FALSE FALSE FALSE
finance from an actuarial perspective
GARCH Model
generalized
Generalized Pareto
Generalized Pareto Distribution
how to use R to deal with computational issues of nonlife insurance
insurance risk modeling
Kaplan Meier Estimator
KML File
LC Method
LC Model
life
linear
log
Min 1Q Median 3Q Max
modeling stock prices
modelling
NA NA
NA NA NA
NA NA NA NA
NA NA NA NA NA
Nelson Siegel Model
normal
Null Deviance
portfolio optimization
Probability Probability Plot
Quantile Function
reinsurance strategies
Return Level Plot
Robust Covariance Estimator
Roc Curve
snippet
spatial statistics applications
Statistics for Finance
survival analysis methods
table
time series forecasting

Product details

  • ISBN 9781466592599
  • Weight: 1480g
  • Dimensions: 178 x 254mm
  • Publication Date: 26 Aug 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

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).
About

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