In this chapter, we reviewed some important concepts of time series analysis, such as cointegration, vector-autoregression, and GARCH-type conditional volatility models. Meanwhile, we have provided a useful introduction to some tips and tricks to start modeling with R for quantitative and empirical finance. We hope that you find these exercises useful, but again, it should be noted that this chapter is far from being complete both from time series and econometric theory, and from R programming's point of view. The R programming language is very well documented on the Internet, and the R user's community consists of thousands of advanced and professional users. We encourage you to go beyond books, be a self-learner, and do not stop if you are stuck with a problem; almost certainly, you will find an answer on the Internet to proceed. Use the documentation of R packages and the help files heavily, and study the official R-site, http://cran.r-project.org/, frequently. The remaining chapters will provide you with numerous additional examples to find your way in the plethora of R facilities, packages, and functions.
Mastering R for Quantitative Finance
Mastering R for Quantitative Finance
Overview of this book
Table of Contents (20 chapters)
Mastering R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Time Series Analysis
Factor Models
Forecasting Volume
Big Data – Advanced Analytics
FX Derivatives
Interest Rate Derivatives and Models
Exotic Options
Optimal Hedging
Fundamental Analysis
Technical Analysis, Neural Networks, and Logoptimal Portfolios
Asset and Liability Management
Capital Adequacy
Systemic Risks
Index
Customer Reviews