Introduction to R for Quantitative Finance by Vidovics-Dancs, Agnes -- Read -- Imperial Library of Trantor

Index

Introduction to R for Quantitative Finance

Table of Contents Introduction to R for Quantitative Finance Credits About the Authors About the Reviewers www.PacktPub.com

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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

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