R Programming for Actuarial Science

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.

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