Introduction to R for Quantitative Finance电子书

Introduction to R for Quantitative Finance

Table of Contents

Introduction to R for Quantitative Finance

Credits

About the Authors

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Time Series Analysis

Working with time series data

Linear time series modeling and forecasting

Modeling and forecasting UK house prices

Model identification and estimation

Model diagnostic checking

Forecasting

Cointegration

Cross hedging jet fuel

Modeling volatility

Volatility forecasting for risk management

Testing for ARCH effects

GARCH model specification

GARCH model estimation

Backtesting the risk model

Forecasting

Summary

2. Portfolio Optimization

Mean-Variance model

Solution concepts

Theorem (Lagrange)

Working with real data

Tangency portfolio and Capital Market Line

Noise in the covariance matrix

When variance is not enough

Summary

3. Asset Pricing Models

Capital Asset Pricing Model

Arbitrage Pricing Theory

Beta estimation

Data selection

Simple beta estimation

Beta estimation from linear regression

Model testing

Data collection

Modeling the SCL

Testing the explanatory power of the individual variance

Summary

4. Fixed Income Securities

Measuring market risk of fixed income securities

Example – implementation in R

Immunization of fixed income portfolios

Net worth immunization

Target date immunization

Dedication

Pricing a convertible bond

Summary

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

6. Derivatives Pricing

The Black-Scholes model

The Cox-Ross-Rubinstein model

Connection between the two models

Greeks

Implied volatility

Summary

7. Credit Risk Management

Credit default models

Structural models

Intensity models

Correlated defaults – the portfolio approach

Migration matrices

Getting started with credit scoring in R

Summary

8. Extreme Value Theory

Theoretical overview

Application – modeling insurance claims

Exploratory data analysis

Tail behavior of claims

Determining the threshold

Fitting a GPD distribution to the tails

Quantile estimation using the fitted GPD model

Calculation of expected loss using the fitted GPD model

Summary

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

A. References

Time series analysis

Portfolio optimization

Asset pricing

Fixed income securities

Estimating the term structure of interest rates

Derivatives Pricing

Credit risk management

Extreme value theory

Financial networks

Index