Learning quantitative finance with R: implement machine learning, time-series analysis, algorithmic trading and more
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing
[2017]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | vi, 268 Seiten Diagramme |
| ISBN: | 9781786462411 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804177707942019072 |
|---|---|
| adam_text | Titel: Learning quantitative finance with R
Autor: Jeet, Param
Jahr: 2017
Table of Contents
Preface 1
Chapter 1: Introduction to R 7
The need for R 7
How to download/install R 8
How to install packages 9
Installing directly from CRAN 10
Installing packages manually 10
Data types 10
Vectors 12
Lists 13
Matrices 14
Arrays 14
Factors 15
DataFrames 15
Importing and exporting different data types 16
How to read and write a CSV format file 17
XLSX 18
Web data or online sources of data 19
Databases 20
How to write code expressions 21
Expressions 21
Constant expression 21
Arithmetic expression 21
Conditional expression 22
Functional call expression 23
Symbols and assignments 23
Keywords 24
Naming variables 24
Functions 24
Calling a function without an argument 26
Calling a function with an argument 26
How to execute R programs 27
How to run a saved file through R Window 27
How to source R script 27
Loops (for, while, if, and if...else) 28
if statement 29
if...else statement 29
for loop 30
while loop 30
apply() 31
sapply() 31
Loop control statements 31
break 31
next 32
Questions 32
Summary 32
Chapter 2: Statistical Modeling 35
Probability distributions 35
Normal distribution 36
norm 36
pnorm 38
qnorm 38
rnorm 38
Lognormal distribution 39
dlnorm 39
plnorm 40
qlnorm 42
rlnorm 42
Poisson distribution 42
Uniform distribution 43
Extreme value theory 43
Sampling 46
Random sampling 46
Stratified sampling 47
Statistics 48
Mean 48
Median 49
Mode 49
Summary 49
Moment 49
Kurtosis 50
Skewness 50
Correlation 51
Autocorrelation 51
Partial autocorrelation 52
Cross-correlation 53
Hypothesis testing 54
Lower tail test of population mean with known variance 54
Upper tail test of population mean with known variance 56
Two-tailed test of population mean with known variance 57
Lower tail test of population mean with unknown variance 58
Upper tail test of population mean with unknown variance 59
Two tailed test of population mean with unknown variance 60
Parameter estimates 61
Maximum likelihood estimation 61
Linear model 63
Outlier detection 64
BoxplOt 64
LOF algorithm 65
Standardization 67
Normalization 67
Questions 68
Summary 68
Chapter 3: Econometric and Wavelet Analysis 69
Simple linear regression 70
Scatter plot 71
Coefficient of determination 72
Significance test 72
Confidence interval for linear regression model 73
Residual plot 74
Normality distribution of errors 75
Multivariate linear regression 76
Coefficient of determination 78
Confidence interval 78
Multicollinearity 78
ANOVA 79
Feature selection 81
Removing irrelevant features 81
Stepwise variable selection 82
Variable selection by classification 83
Ranking of variables 84
Wavelet analysis 85
Fast Fourier transformation 91
Hilbert transformation 93
Questions 96
Summary 97
Chapter 4: Time Series Modeling 99
General time series 100
Converting data to time series 101
ZOO 103
Constructing a zoo object 103
Reading an external file using zoo 104
Advantages of a zoo object 105
Subsetting the data 105
Merging zoo objects 105
Plotting zoo objects 106
Disadvantages of a zoo object 106
xts 107
Construction of an xts object using as.xts 107
Constructing an xts object from scratch 108
Linear filters 108
AR 110
MA 111
ARIMA 113
GARCH 120
EGARCH 122
VGARCH 123
Dynamic conditional correlation 125
Questions 127
Summary 127
Chapter 5: Algorithmic Trading 129
Momentum or directional trading 130
Pairs trading 140
Distance-based pairs trading 141
Correlation based pairs trading 147
Co-integration based pairs trading 151
Capital asset pricing model 155
Multi factor model 157
Portfolio construction 162
Questions 166
Summary 166
Chapter 6: Trading Using Machine Learning 167
Logistic regression neural network 168
Neural network 175
Deep neural network 183
K means algorithm 186
K nearest neighborhood 188
Support vector machine 192
Decision tree 194
Random forest 197
Questions 201
Summary 201
Chapter 7: Risk Management 203
Market risk 203
Portfolio risk 205
VaR 209
Parametric VaR 209
Historical VaR 211
Monte Carlo simulation 213
Hedging 215
Basel regulation 216
Credit risk 217
Fraud detection 223
Liability management 225
Questions 226
Summary 226
Chapter 8: Optimization 227
Dynamic rebalancing 228
Periodic rebalancing 228
Walk forward testing 232
Grid testing 233
Genetic algorithm 236
Questions 241
Summary 241
Chapter 9: Derivative Pricing 243
Option pricing 243
Black-Scholes model 244
Cox-Ross-Rubinstein model 245
Greeks 248
Implied volatility 250
Bond pricing 250
Credit spread 253
Credit default swaps 256
Interest rate derivatives 257
Exotic options 258
Questions 263
Summary 263
Index 265
Table of Contents
Preface 1
Chapter 1: Introduction to R______________________________________________________7
The need for R 7
How to download/install R 8
How to install packages 9
Installing directly from CRAN 10
Installing packages manually 10
Data types 10
Vectors 12
Lists 13
Matrices 14
Arrays 14
Factors 15
DataFrames 15
Importing and exporting different data types 16
How to read and write a CSV format file 17
XLSX 18
Web data or online sources of data 19
Databases 20
How to write code expressions 21
Expressions 21
Constant expression 21
Arithmetic expression 21
Conditional expression 22
Functional call expression 23
Symbols and assignments 23
Keywords 24
Naming variables 24
Functions 24
Calling a function without an argument 26
Calling a function with an argument 26
How to execute R programs 27
How to run a saved file through R Window 27
How to source R script 27
Loops (for, while, if, and if...else) 28
if statement 29
if...else statement 29
for loop 30
while loop 30
apply() 31
sapply() 31
Loop control statements 31
break 31
next 32
Questions 32
Summary 32
Chapter 2: Statistical Modeling___________________________________________________35
Probability distributions 35
Normal distribution 36
norm 36
pnorm 38
qnorm 38
rnorm 38
Lognormal distribution 39
dlnorm 39
plnorm 40
qlnorm 42
rlnorm 42
Poisson distribution 42
Uniform distribution 43
Extreme value theory 43
Sampling 46
Random sampling 46
Stratified sampling 47
Statistics 48
Mean 48
Median 49
Mode 49
Summary 49
Moment 49
Kurtosis 50
Skewness 50
Correlation 51
Autocorrelation 51
Partial autocorrelation 52
Cross-correlation 53
Hypothesis testing 54
Lower tail test of population mean with known variance 54
Upper tail test of population mean with known variance 56
Two-tailed test of population mean with known variance 57
Lower tail test of population mean with unknown variance 58
Upper tail test of population mean with unknown variance 59
Two tailed test of population mean with unknown variance 60
Parameter estimates 61
Maximum likelihood estimation 61
Linear model 63
Outlier detection 64
Boxplot 64
LOF algorithm 65
Standardization 67
Normalization 67
Questions 68
Summary 68
Chapter 3: Econometric and Waveiet Analysis_____________________________________69
Simple linear regression 70
Scatter plot 71
Coefficient of determination 72
Significance test 72
Confidence interval for linear regression model 73
Residual plot 74
Normality distribution of errors 75
Multivariate linear regression 76
Coefficient of determination 78
Confidence interval 78
Multicollinearity 78
ANOVA 79
Feature selection 81
Removing irrelevant features 81
Stepwise variable selection 82
Variable selection by classification 83
Ranking of variables 84
Wavelet analysis 85
Fast Fourier transformation 91
Hilbert transformation 93
[iii]
Questions 96
Summary 97
Chapter 4: Time Series Modeling_______________________________________________99
General time series 100
Converting data to time series 101
ZOO 103
Constructing a zoo object 103
Reading an external file using zoo 104
Advantages of a zoo object 105
Subsetting the data 105
Merging zoo objects 105
Plotting zoo objects 106
Disadvantages of a zoo object 106
xts 107
Construction of an xts object using as.xts 107
Constructing an xts object from scratch 108
Linear filters 108
AR 110
MA 111
ARIMA 113
GARCH 120
EGARCH 122
VGARCH 123
Dynamic conditional correlation 125
Questions 127
Summary 127
Chapter 5: Algorithmic Trading_______________________________________________129
Momentum or directional trading 130
Pairs trading 140
Distance-based pairs trading 141
Correlation based pairs trading 147
Co-integration based pairs trading 151
Capital asset pricing model 155
Multi factor model 157
Portfolio construction 162
Questions 166
Summary 166
Chapter 6: Trading Using Machine Learning____________________________________167
Logistic regression neural network 168
------------------------------------ [iv] -------------------------------------
Neural network 175
Deep neural network 183
K means algorithm 186
K nearest neighborhood 188
Support vector machine 192
Decision tree 194
Random forest 197
Questions 201
Summary 201
Chanter 7: Risk Management 203
Market risk 203
Portfolio risk 205
VaR 209
Parametric VaR 209
Historical VaR 211
Monte Carlo simulation 213
Hedging 215
Basel regulation 216
Credit risk 217
Fraud detection 223
Liability management 225
Questions 226
Summary 226
Chapter 8: Optimization 227
Dynamic rebalancing 228
Periodic rebalancing 228
Walk forward testing 232
Grid testing 233
Genetic algorithm 236
Questions 241
Summary 241
Chapter 9: Derivative Pricing 243
Option pricing 243
Black-Scholes model 244
Cox-Ross-Rubinstein model 245
Greeks 248
Implied volatility 250
Bond pricing 250
[v]
Crédit spread 253
Crédit default swaps 256
Interest rate dérivatives 257
Exotic options 258
Questions 263
Summary 263
Index___________________________________________________________________265
[vi]
|
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| spellingShingle | Jeet, Param Vats, Prashant Learning quantitative finance with R implement machine learning, time-series analysis, algorithmic trading and more R Programm (DE-588)4705956-4 gnd Finanzmathematik (DE-588)4017195-4 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4017195-4 |
| title | Learning quantitative finance with R implement machine learning, time-series analysis, algorithmic trading and more |
| title_auth | Learning quantitative finance with R implement machine learning, time-series analysis, algorithmic trading and more |
| title_exact_search | Learning quantitative finance with R implement machine learning, time-series analysis, algorithmic trading and more |
| title_full | Learning quantitative finance with R implement machine learning, time-series analysis, algorithmic trading and more Param Jeet, Prashant Vats |
| title_fullStr | Learning quantitative finance with R implement machine learning, time-series analysis, algorithmic trading and more Param Jeet, Prashant Vats |
| title_full_unstemmed | Learning quantitative finance with R implement machine learning, time-series analysis, algorithmic trading and more Param Jeet, Prashant Vats |
| title_short | Learning quantitative finance with R |
| title_sort | learning quantitative finance with r implement machine learning time series analysis algorithmic trading and more |
| title_sub | implement machine learning, time-series analysis, algorithmic trading and more |
| topic | R Programm (DE-588)4705956-4 gnd Finanzmathematik (DE-588)4017195-4 gnd |
| topic_facet | R Programm Finanzmathematik |
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