Introduction to R for Quantitative Finance

R is a statistical computing language that’s ideal for answering quantitative finance questions. This book gives you both theory and practice, all in clear language with stacks of real-world examples. Ideal for R beginners or expert alike.
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Introduction to R for Quantitative Finance

Gergely Daróczi et al.

1 customer reviews
R is a statistical computing language that’s ideal for answering quantitative finance questions. This book gives you both theory and practice, all in clear language with stacks of real-world examples. Ideal for R beginners or expert alike.
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Book Details

ISBN 139781783280933
Paperback164 pages

Book Description

Introduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.

This book will be your guide on how to use and master R in order to solve quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.

Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives such as credit risk management.

Table of Contents

Chapter 1: Time Series Analysis
Working with time series data
Linear time series modeling and forecasting
Cointegration
Modeling volatility
Summary
Chapter 2: Portfolio Optimization
Mean-Variance model
Solution concepts
Working with real data
Tangency portfolio and Capital Market Line
Noise in the covariance matrix
When variance is not enough
Summary
Chapter 3: Asset Pricing Models
Capital Asset Pricing Model
Arbitrage Pricing Theory
Beta estimation
Model testing
Summary
Chapter 4: Fixed Income Securities
Measuring market risk of fixed income securities
Immunization of fixed income portfolios
Pricing a convertible bond
Summary
Chapter 5: Estimating the Term Structure of Interest Rates
The term structure of interest rates and related functions
The estimation problem
Estimation of the term structure by linear regression
Cubic spline regression
Applied R functions
Summary
Chapter 6: Derivatives Pricing
The Black-Scholes model
The Cox-Ross-Rubinstein model
Connection between the two models
Greeks
Implied volatility
Summary
Chapter 7: Credit Risk Management
Credit default models
Correlated defaults – the portfolio approach
Migration matrices
Getting started with credit scoring in R
Summary
Chapter 8: Extreme Value Theory
Theoretical overview
Application – modeling insurance claims
Summary
Chapter 9: Financial Networks
Representation, simulation, and visualization of financial networks
Analysis of networks’ structure and detection of topology changes
Contribution to systemic risk – identification of SIFIs
Summary

What You Will Learn

  • How to model and forecast house prices and improve hedge ratios using cointegration and model volatility
  • How to understand the theory behind portfolio selection and how it can be applied to real-world data
  • How to utilize the Capital Asset Pricing Model and the Arbitrage Pricing Theory
  • How to understand the basics of fixed income instruments
  • You will discover how to use discrete- and continuous-time models for pricing derivative securities
  • How to successfully work with credit default models and how to model correlated defaults using copulas
  • How to understand the uses of the Extreme Value Theory in insurance and fi nance, model fitting, and risk measure calculation

Authors

Table of Contents

Chapter 1: Time Series Analysis
Working with time series data
Linear time series modeling and forecasting
Cointegration
Modeling volatility
Summary
Chapter 2: Portfolio Optimization
Mean-Variance model
Solution concepts
Working with real data
Tangency portfolio and Capital Market Line
Noise in the covariance matrix
When variance is not enough
Summary
Chapter 3: Asset Pricing Models
Capital Asset Pricing Model
Arbitrage Pricing Theory
Beta estimation
Model testing
Summary
Chapter 4: Fixed Income Securities
Measuring market risk of fixed income securities
Immunization of fixed income portfolios
Pricing a convertible bond
Summary
Chapter 5: Estimating the Term Structure of Interest Rates
The term structure of interest rates and related functions
The estimation problem
Estimation of the term structure by linear regression
Cubic spline regression
Applied R functions
Summary
Chapter 6: Derivatives Pricing
The Black-Scholes model
The Cox-Ross-Rubinstein model
Connection between the two models
Greeks
Implied volatility
Summary
Chapter 7: Credit Risk Management
Credit default models
Correlated defaults – the portfolio approach
Migration matrices
Getting started with credit scoring in R
Summary
Chapter 8: Extreme Value Theory
Theoretical overview
Application – modeling insurance claims
Summary
Chapter 9: Financial Networks
Representation, simulation, and visualization of financial networks
Analysis of networks’ structure and detection of topology changes
Contribution to systemic risk – identification of SIFIs
Summary

Book Details

ISBN 139781783280933
Paperback164 pages
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