Professional resource providing an introduction to R coding for actuarial and financial mathematics applications, with real-life examples

R Programming for Actuarial Scienceprovides a grounding in R programming applied to the mathematical and statistical methods that are of relevance for actuarial work.

InR 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 Scienceis 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.

Peter McQuire, FIA, is a Lecturer in Actuarial Science at the University of Kent. He has 18 years of experience in pension scheme consultancy and risk management, and more than 10 years teaching at the University. He is a Fellow of the Institute and Faculty of Actuaries.

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.

About the Companion Website xxi

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