Contents of this OLC
- Downloadable lecture slides (PDF and LaTeX available)
- Accompanying figure files
- Downloadable R code (.txt format)
Table of Contents
Chapter 1 – Introduction
Chapter 2 – Summary statistics and elementary data presentation
Chapter 3 – Basic hypothesis tests
Chapter 4 – An introduction to regression
Chapter 5 – The extra sum of squares principle and regression modelling assumptions
Chapter 6 – Violations of regression modelling assumptions – autocorrelation
Chapter 7 – Violations of regression modelling assumptions – multicollinearity
Chapter 8 – Dummy variable regression models
Chapter 9 – Qualitative response regression models
Chapter 10 – Linear mixed and generalised linear mixed models
Chapter 11 – Non-financial time series models
Chapter 12 – Modelling financial price data
Chapter 13 – ARCH/GARCH models
About this book
“This is a fantastic book for anyone wanting to understand, learn and apply quantitative methods in finance using R” Professor Raphael Markellos, Professor of Finance, Norwich Business School, UK
Quantitative Methods in Finance Using R draws on the extensive teaching and research expertise of John Fry and Matt Burke, covering a wide range of quantitative methods in Finance that utilise the freely downloadable R software. With software playing an increasingly important role in finance, this book is a must-have introduction for finance students who want to explore how they can undertake their own quantitative analyses in dissertation and project work.
Assuming no prior knowledge, and taking a holistic approach, this brand new title guides you from first principles and help to build your confidence in tackling large data sets in R.
Complete with examples and exercises with worked solutions, Fry and Burke demonstrate how to use the R freeware for regression and linear modelling, with attention given to presentation and the importance of good writing and presentation skills in project work and data analysis more generally.
Through this book, you will develop your understanding of:
- Descriptive statistics
- Inferential statistics
- Regression
- Analysis of variance
- Probability regression models
- Mixed models
- Financial and non-financial time series
John Fry is a senior lecturer in Applied Mathematics at the University of Hull, UK. Fry has a PhD in Mathematical Finance from the University of Sheffield, UK. His main research interests span mathematical finance, econophysics, statistics and operations research.
Matt Burke is a senior lecturer in Finance at Sheffield Hallam University, UK. He holds a PhD in Finance from the University of East Anglia, UK. Burke’s main research interests lie in asset pricing and climate finance.
Praise for the book:
“This is a fantastic book for anyone wanting to understand, learn and apply quantitative methods in finance using R. Instructors, students, researchers and practitioners in finance are increasingly using R for data analysis as it is powerful, relatively easy to learn and free. It has a passionate worldwide community of users that share advice and programmes (known as R packages), much of this is specific to finance. The book is organised in twelve lectures that cover standard topics in descriptive statistics, hypothesis testing, regression and financial econometrics. These are well supported by engaging examples, solved tutorial exercises, datasets and slides. The intended audience is undergraduate and masters level students. The book is written in plain English and avoids unnecessary mathematical details. No prior knowledge of statistics, econometrics or programming (in R or other languages) is assumed.”
Professor Raphael Markellos, Professor of Finance, Norwich Business School, UK
“This book will be hugely popular in the field of Finance as it provides a hands on guide to how to address topics facing financial academics, managers and practitioners in an easy to understand, logically laid out and coherent form. The example datasets, graphical analysis, tutorial sessions and use of R software make the book more versatile for use across disciplines. The book will form a solid foundation to support the transition of Finance students into the world of work or further research.”
Professor Jane M Binner, Chair of Finance, Department of Finance, University of Birmingham, UK
“The textbook by John Fry and Matt Burke takes a highly hands-on approach in teaching quantitative finance. It introduces fundamental, intermediate, and some advanced statistical concepts and modelling techniques and illustrates them with ample working examples relevant to professional practice in this discipline. The book uses the R programming language as the software tool for practical work, which is a good choice for students without a prior background in programming. The book can be highly recommended to undergraduate and post-graduate students in finance, economics, and business analytics. It will also be of much interest to professionals in related fields, wishing to gain the knowledge in statistical and econometric techniques as well as in practical data analysis.”
Viktor Pekar, Lecturer in Business Analytics, Aston University, UK
“Fry and Burke have done a marvellous job in assembling a package for students and lecturers alike to achieve the effective learning and teaching of this important subject. The topics included have been carefully selected and sequenced and the explanations pitched at the right level. Solutions to all exercises have been included which is a real bonus. Furthermore, links to the website offer additional resources for the enthusiastic learner/teacher. In over 20 years of teaching quantitative methods, I have rarely come across a book such as this which meets/exceeds all the expectations of its intended audience so well. I recommend this book very highly indeed!”
Tuan Yu, Lecturer, Kent Business School, Canterbury, UK
“Quantitative Methods in Finance using R equips readers with a practical set of modelling skills readily applicable to financial data. For those new to R (a great addition to any quant CV), commands are easy to follow and replicate. Detection of, and remedies for, violations of common regression model assumptions are described clearly, and more advanced topics in time series are presented with appropriate technical detail as well as a thorough commentary. For those seeking to make sense of the data deluge in finance, this text offers readers the chance to navigate the data waves with confidence.”
Dr James Abdey, Associate Professorial Lecturer in Statistics, London School of Economics and Political Science, UK
Order your copy
If you are an instructor considering adopting this book for your course, you can order your inspection copy here; if you are a student, or indeed, anyone else interested in the book, you can buy a copy in print or eBook form here.
