更新时间：2021-07-23 14:14:13
coverpage
Introduction to R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Support files eBooks discount offers and more
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
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
Chapter 3. Asset Pricing Models
Capital Asset Pricing Model
Arbitrage Pricing Theory
Beta estimation
Model testing
Chapter 4. Fixed Income Securities
Measuring market risk of fixed income securities
Immunization of fixed income portfolios
Pricing a convertible bond
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
Chapter 6. Derivatives Pricing
The Black-Scholes model
The Cox-Ross-Rubinstein model
Connection between the two models
Greeks
Implied volatility
Chapter 7. Credit Risk Management
Credit default models
Correlated defaults – the portfolio approach
Migration matrices
Getting started with credit scoring in R
Chapter 8. Extreme Value Theory
Theoretical overview
Application – modeling insurance claims
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
Appendix 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
