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R Programming for Actuarial Science by Peter McQuire PDF

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.


Table of Contents
R : What You Need to Know to Get Started
Functions in R
Financial Mathematics (1): Interest Rates and Valuing Cashflows
Financial Mathematics (2): Miscellaneous Examples
Fundamental Statistics: A Selection of Key Topics -- Dr A Kume
Multivariate Distributions, and Sums of Random Variables
Benefits of Diversification
Modern Portfolio Theory
Duration -- A Measure of Interest Rate Sensitivity
Asset-Liability Matching: An Introduction
Hedging: Protecting Against a Fall in Equity Markets
Immunisation -- Redington and Beyond
Copulas
Copulas -- A Modelling Exercise
Bond Portfolio Valuation: A Simple Credit Risk Model
The Markov 2-State Mortality Model
Approaches to Fitting Mortality Models: The Markov 2-state Model and an Introduction to Splines
Assessing the Suitability of Mortality Models: Statistical Tests
The Lee-Carter Model
The Kaplan-Meier Estimator
Cox Proportionate Hazards Regression Model
Markov Multiple State Models: Applications to Life Contingencies
Contingencies I
Contingencies II
Actuarial Risk Theory -- An Introduction: Collective and Individual Risk Models
Collective Risk Models: Exercise
Generalised Linear Models: Poisson Regression
Extreme Value Theory
Introduction to Machine Learning: k-Nearest Neighbours (kNN)
Time Series Modelling in R -- Dr A Kume
Volatility Models -- GARCH
Modelling Future Stock Prices Using Geometric Brownian Motion: An Introduction
Financial Options: Pricing, Characteristics, and Strategies

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