R Programming for Actuarial Science by Peter McQuire PDF

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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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