R In Finance And Economics: A Beginner's Guide

Hardcover

\$68.00
• Want it by Thursday, September 27  Order now and choose Expedited Shipping during checkout.

Overview

R In Finance And Economics: A Beginner's Guide by Abhay Kumar Singh, David Edmund Allen

TEXTBOOK

Product Details

ISBN-13: 9789813144460 World Scientific Publishing Company, Incorporated 02/03/2017 264 6.10(w) x 9.10(h) x 0.70(d)

Preface vii

Supplementary Material xi

1 Introduction 1

1.1 Why Should We Learn R? 2

1.2 R: Getting Started 3

1.2.1 Installing R on windows 3

1.2.2 RStudio: A better way to run R 4

1.2.3 Installing RStudio for windows 4

1.2.4 RStudio GUI/IDE 5

1.3 Proxy Setup 6

1.4 R Packages 7

1.4.1 Installing packages 8

1.5 R Core Packages 8

1.6 Task Views in R: Introduction and Installation 9

1.7 Getting Help 10

1.7.1 Getting help from the web 13

1.8 Summary 14

2 Data Objects in R 15

2.1 Introduction 15

2.2 Data Types 15

2.2.1 Double 16

2.2.2 Integer 16

2.2.3 Complex 16

2.2.4 Logical 17

2.2.5 Character 17

2.2.6 Factor 18

2.2.7 Date and time 18

2.2.8 Missing data in R 20

2.3 Data Structures in R 20

2.3.1 Vector 20

2.3.2 Matrices 21

2.3.2.1 Matrix manipulations 22

2.3.3 Arrays 23

2.3.4 Data frames 25

2.3.5 Lists 27

2.4 Summary 28

3 Data Handling in R 29

3.1 Introduction 29

3.2 Importing and Exporting Tabular Data in R 29

3.2.1 Reading data, from a text file 29

3.2.2 Reading data from CSV file 32

3.2.3 Reading data from excel files 33

3.2.4 Reading data from databases 33

3.2.5 Reading from data files from other statistical systems 33

3.2.5.1 Reading SPSS data file 34

3.2.5.2 Reading Stata data file 36

3.2.5.3 Reading Matlab data files 37

3.3 Data Preprocessing in R 37

3.3.1 Extracting data 37

3.3.2 Combining data frames 39

3.3.3 Sub-setting and logical data selection 41

3.4 Summary 43

4 R Programming and Control Flow 45

4.1 Introduction 45

4.2 Control Flow 46

4.2.1 If-else conditional statements 46

4.2.2 Using switch 47

4.2.3 Loops 48

4.3 Functions in R 50

4.4 Summary 52

5 Data Exploration 53

5.1 Introduction 53

5.2 Summary Statistics 53

5.3 Example: Descriptive Statistics of Stock Returns 59

5.3.1 Introduction 59

5.3.2 Importing the data 61

5.3.2.1 Using the describe function 66

5.3.2.2 Using stat.desc from package pastees 67

5.3.3 Some basic plots 71

5.4 Summary 74

6 Graphics in R 75

6.1 Introduction 75

6.2 Basic Plots in R 76

6.3 Exporting Graphics 86

6.4 R. Graphical Parameters 88

6.5 Introduction to ggplot2 91

6.5.1 Getting started with qplot 92

6.5.2 Layered graphics using ggplot 93

6.6 Transforming Data from Wide to Long Format 100

6.7 Summary 102

7 Regression Analysis-I 103

7.1 Introduction 103

7.2 OLS 103

7.3 QR 107

7.3.1 Estimating QR 110

7.4 Example: Fama-French Factor Model and Multiple Regression 114

7.4.1 Introduction 114

7.4.2 Data 116

7.4.2.1 Data preprocessing 117

7.4.3 OLS regression analysis of the Fama French three factor model 121

7.4.4 Quantile analysis of the Fama French three factor model 124

7.5 Summary 129

8 Regression Analysis-II 131

8.1 Introduction 131

8.2 Panel Data Linear Regression 131

8.2.1 Fixed and Random effects using the plm package 133

8.2.1.1 Fixed effect estimation 134

8.2.1.2 Random effect estimation 136

8.2.1.3 Panel or OLS 137

8.2.1.4 Fixed effect or random effect 139

8.3 Logistic Regression 139

8.4 Example: Economic Growth and Unemployment - A Panel Analysis 142

8.4.1 Data and methodology 142

8.4.2 Data preprocessing 143

8.4.3 Linear panel regression analysis 145

8.5 Summary 151

9 Time Series Analysis 153

9.1 Introduction 153

9.2 Time Series-Some Properties 153

9.2.1 Stochastic process 153

9.2.2 Stationary 154

9.2.3 Autocorrelation function (ACF) 157

9.2.4 White noise 159

9.3 Autoregressive Moving Average Model (ARMA) 159

9.3.1 Fitting an ARMA model 161

9.4 Volatility Modelling using Generalised Autoregressive Conditional Heteroskedasticity (GARCH) 166

9.4.1 Fitting a GARCH(1,1) model using the rugarch package 167

9.5 Example: Modelling and Forecasting Daily VaR using GARCH 175

9.5.1 Data and methodology 175

9.5.2 VaR forecasts 178

9.6 Summary 182

10 Extreme Value Theory Modelling 183

10.1 Introduction 183

10.2 EVT and Financial Risk Modelling 183

10.2.1 GPD and POT methods 185

10.2.1.1 Sample mean excess plot 186

10.2.1.2 Estimation of GPD 187

10.2.1.3 VaR and expected shortfall 191

10.3 EVT and Tail Dependence 195

10.3.1 Measures of tail dependence 195

10.3.1.1 Asymptotic dependence - the conventional approach 197

10.3.1.2 Asymptotic independence - an alternative measure of dependence 198

10.3.2 Estimating v and X- nonparametric method 202

10.4 Example: EVT VaR A Dynamic Approach 204

10.4.1 Data and methodology 206

10.4.2 VaR forecasts using dynamic EVT model 207

10.5 Summary 209

11 Introduction to Multivariate Analysis using Copulas 211

11.1 Introduction 211

11.2 Copula 212

11.2.1 Types of copula 213

11.2.1.1 Elliptical copulas 213

11.2.1.2 Archimedean copulas 217

11.2.2 Copula selection 222

11.3 Multivariate Vine Copulas 222

11.3.1 Estimating R-Vine copulas using R 224

11.4 Example: Portfolio VaR Estimation using Vine Copula 229

11.4.1 Data and methodology 229

11.4.2 VaR forecasts using R 230

11.5 Summary 234

Bibliography 235

Index 243

Average Review