Learning Quantitative Finance with R

Implement machine learning, time-series analysis, algorithmic trading and more
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Learning Quantitative Finance with R

Dr. Param Jeet, Prashant Vats

1 customer reviews
Implement machine learning, time-series analysis, algorithmic trading and more

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Book Details

ISBN 139781786462411
Paperback284 pages

Book Description

The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language.

You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate.

We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging.

By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.

Table of Contents

Chapter 1: Introduction to R
The need for R
How to download/install R
How to install packages
Data types
Importing and exporting different data types
How to write code expressions
Functions
Loops (for, while, if, and if...else)
Loop control statements
Questions
Summary
Chapter 2: Statistical Modeling
Probability distributions
Sampling
Statistics
Correlation
Hypothesis testing
Parameter estimates
Outlier detection
Standardization
Normalization
Questions
Summary
Chapter 3: Econometric and Wavelet Analysis
Simple linear regression
Multivariate linear regression
Multicollinearity
ANOVA
Feature selection
Stepwise variable selection
Ranking of variables
Wavelet analysis
Fast Fourier transformation
Hilbert transformation
Questions
Summary
Chapter 4: Time Series Modeling
General time series
Converting data to time series
zoo
xts
Linear filters
AR
MA
ARIMA
GARCH
EGARCH
VGARCH
Dynamic conditional correlation
Questions
Summary
Chapter 5: Algorithmic Trading
Momentum or directional trading
Capital asset pricing model
Multi factor model
Portfolio construction
Questions
Summary
Chapter 6: Trading Using Machine Learning
Logistic regression neural network
Neural network
Deep neural network
K means algorithm
K nearest neighborhood
Support vector machine
Decision tree
Random forest
Questions
Summary
Chapter 7: Risk Management
Market risk
Portfolio risk
VaR
Monte Carlo simulation
Hedging
Basel regulation
Credit risk
Fraud detection
Liability management
Questions
Summary
Chapter 8: Optimization
Dynamic rebalancing
Walk forward testing
Grid testing
Genetic algorithm
Questions
Summary
Chapter 9: Derivative Pricing
Option pricing
Implied volatility
Bond pricing
Credit spread
Credit default swaps
Interest rate derivatives
Exotic options
Questions
Summary

What You Will Learn

  • Get to know the basics of R and how to use it in the field of Quantitative Finance
  • Understand data processing and model building using R
  • Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis
  • Build and analyze quantitative finance models using real-world examples
  • How real-life examples should be used to develop strategies
  • Performance metrics to look into before deciding upon any model
  • Deep dive into the vast world of machine-learning based trading
  • Get to grips with algorithmic trading and different ways of optimizing it
  • Learn about controlling risk parameters of financial instruments

Authors

Table of Contents

Chapter 1: Introduction to R
The need for R
How to download/install R
How to install packages
Data types
Importing and exporting different data types
How to write code expressions
Functions
Loops (for, while, if, and if...else)
Loop control statements
Questions
Summary
Chapter 2: Statistical Modeling
Probability distributions
Sampling
Statistics
Correlation
Hypothesis testing
Parameter estimates
Outlier detection
Standardization
Normalization
Questions
Summary
Chapter 3: Econometric and Wavelet Analysis
Simple linear regression
Multivariate linear regression
Multicollinearity
ANOVA
Feature selection
Stepwise variable selection
Ranking of variables
Wavelet analysis
Fast Fourier transformation
Hilbert transformation
Questions
Summary
Chapter 4: Time Series Modeling
General time series
Converting data to time series
zoo
xts
Linear filters
AR
MA
ARIMA
GARCH
EGARCH
VGARCH
Dynamic conditional correlation
Questions
Summary
Chapter 5: Algorithmic Trading
Momentum or directional trading
Capital asset pricing model
Multi factor model
Portfolio construction
Questions
Summary
Chapter 6: Trading Using Machine Learning
Logistic regression neural network
Neural network
Deep neural network
K means algorithm
K nearest neighborhood
Support vector machine
Decision tree
Random forest
Questions
Summary
Chapter 7: Risk Management
Market risk
Portfolio risk
VaR
Monte Carlo simulation
Hedging
Basel regulation
Credit risk
Fraud detection
Liability management
Questions
Summary
Chapter 8: Optimization
Dynamic rebalancing
Walk forward testing
Grid testing
Genetic algorithm
Questions
Summary
Chapter 9: Derivative Pricing
Option pricing
Implied volatility
Bond pricing
Credit spread
Credit default swaps
Interest rate derivatives
Exotic options
Questions
Summary

Book Details

ISBN 139781786462411
Paperback284 pages
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From 1 reviews

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