Use R to optimize your trading strategy and build up your own risk management system
About This Book
Learn to manipulate, visualize, and analyze a wide range of financial data with the help of built-in functions and programming in R
Understand the concepts of financial engineering and create trading strategies for complex financial instruments
Explore R for asset and liability management and capital adequacy modeling
Who This Book Is For
This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.
What You Will Learn
Analyze high frequency financial data
Build, calibrate, test, and implement theoretical models such as cointegration, VAR, GARCH, APT, Black-Scholes, Margrabe, logoptimal portfolios, core-periphery, and contagion
Solve practical, real-world financial problems in R related to big data, discrete hedging, transaction costs, and more.
Discover simulation techniques and apply them to situations where analytical formulas are not available
Create a winning arbitrage, speculation, or hedging strategy customized to your risk preferences
Understand relationships between market factors and their impact on your portfolio
Assess the trade-off between accuracy and the cost of your trading strategy
In Detail
R is a powerful open source functional programming language that provides high level graphics and interfaces to other languages. Its strength lies in data analysis, graphics, visualization, and data manipulation. R is becoming a widely used modeling tool in science, engineering, and business.
The book is organized as a step-by-step practical guide to using R. Starting with time series analysis, you will also learn how to forecast the volume for VWAP Trading. Among other topics, the book covers FX derivatives, interest rate derivatives, and optimal hedging. The last chapters provide an overview on liquidity risk management, risk measures, and more.
The book pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the book, you will be well versed with various financial techniques using R and will be able to place good bets while making financial decisions.
Table of Contents
Chapter 1: Time Series Analysis
Chapter 2: Factor Models
Chapter 3: Forecasting Volume
Chapter 4: Big Data – Advanced Analytics
Chapter 5: FX Derivatives
Chapter 6: Interest Rate Derivatives and Models
Chapter 7: Exotic Options
Chapter 8: Optimal Hedging
Chapter 9: Fundamental Analysis
Chapter 10: Technical Analysis, Neural Networks, and Logoptimal Portfolios
Chapter 11: Asset and Liability Management
Chapter 12: Capital Adequacy
Chapter 13: Systemic Risks
