R Programming for Actuarial Science
1. Edition November 2023
640 Pages, Hardcover
Wiley & Sons Ltd
R Programming for Actuarial Science
Professional resource providing an introduction to R coding for actuarial and financial mathematics applications, with real-life examples
R Programming for Actuarial Science provides a grounding in R programming applied to the mathematical and statistical methods that are of relevance for actuarial work.
In R Programming for Actuarial Science, readers will find:
* Basic theory for each chapter to complement other actuarial textbooks which provide foundational theory in depth.
* Topics covered include compound interest, statistical inference, asset-liability matching, time series, loss distributions, contingencies, mortality models, and option pricing plus many more typically covered in university courses.
* More than 400 coding examples and exercises, most with solutions, to enable students to gain a better understanding of underlying mathematical and statistical principles.
* An overall basic to intermediate level of coverage in respect of numerous actuarial applications, and real-life examples included with every topic.
Providing a highly useful combination of practical discussion and basic theory, R Programming for Actuarial Science is an essential reference for BSc/MSc students in actuarial science, trainee actuaries studying privately, and qualified actuaries with little programming experience, along with undergraduate students studying finance, business, and economics.
Introduction 1
1 R : What You Need to Know to Get Started 9
2 Functions in R 33
3 Financial Mathematics (1): Interest Rates and Valuing Cashflows 45
4 Financial Mathematics (2): Miscellaneous Examples 63
5 Fundamental Statistics: A Selection of Key Topics -- Dr A Kume 87
6 Multivariate Distributions, and Sums of Random Variables 139
7 Benefits of Diversification 147
8 Modern Portfolio Theory 155
9 Duration -- A Measure of Interest Rate Sensitivity 171
10 Asset-Liability Matching: An Introduction 177
11 Hedging: Protecting Against a Fall in Equity Markets 187
12 Immunisation -- Redington and Beyond 195
13 Copulas 211
14 Copulas -- A Modelling Exercise 237
15 Bond Portfolio Valuation: A Simple Credit Risk Model 247
16 The Markov 2-State Mortality Model 259
17 Approaches to Fitting Mortality Models: The Markov 2-state Model and an Introduction to Splines 273
18 Assessing the Suitability of Mortality Models: Statistical Tests 295
19 The Lee-Carter Model 311
20 The Kaplan-Meier Estimator 329
21 Cox Proportionate Hazards Regression Model 339
22 Markov Multiple State Models: Applications to Life Contingencies 351
23 Contingencies I 383
24 Contingencies II 403
25 Actuarial Risk Theory -- An Introduction: Collective and Individual Risk Models 447
26 Collective Risk Models: Exercise 473
27 Generalised Linear Models: Poisson Regression 481
28 Extreme Value Theory 501
29 Introduction to Machine Learning: k-Nearest Neighbours (kNN) 513
30 Time Series Modelling in R -- Dr A Kume 523
31 Volatility Models -- GARCH 551
32 Modelling Future Stock Prices Using Geometric Brownian Motion: An Introduction 571
33 Financial Options: Pricing, Characteristics, and Strategies 585
Index 605
Dr. Alfred Kume is a Senior Lecturer in Statistics at the University of Kent with more than 20 years of teaching experience and exposure to general insurance.
