Modeling Financial Time Series with R
What Is the Book and Why Was It Written?
This book is a guide to analyzing and modeling financial time series using the open source object oriented R statistical programming language. It is a complete re-write of my book with Jiahui Wang Modeling Financial Time Series with S-PLUS, Second Edition. Every chapter has been extensively re-written, new material has been added, and all of the examples are performed with R. The book is a unique blend of econometric theory, financial models, data analysis, and statistical programming using R. It serves as a user's guide for many popular R packages of statistical functions for financial time series analysis and financial econometrics as well as a general reference for models used in applied financial econometrics. The format of the chapters in the book is to give a reasonably complete description of a statistical model and how it works followed by illustrations of how to analyze the model using R. In this way, the book stands alone as an introduction to financial time series analysis as well as a user's guide selected R packages for time series analysis. It also highlights the general analysis of time series data using the zoo and xts classes in R.
This book is written for a wide audience of individuals who work, do research or study in the areas of empirical finance and financial econometrics. The field of financial econometrics has exploded over the last decade, and this book represents an integration of theory, methods and examples using the R modeling language to facilitate the practice of financial econometrics. This audience includes researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Researchers and practitioners in the finance industry who already use R and desire more functionality for analyzing and modeling financial data will find this text useful. It is also appropriate for financial analysts who may not be familiar with R but who desire an integrated and open statistical modeling and programming environment for the analysis of financial data. This guide is useful for academic researchers interested in empirical finance and financial econometrics. Finally, this book may be used as a textbook or a textbook companion for advanced MBA and graduate level courses in time series analysis, empirical finance and financial econometrics.
It is assumed that the reader has a basic familiarity with R at the level presented in A Beginner's Guide to R and a background in mathematical statistics at the level of Hogg and Craig (1994), is comfortable with linear algebra and linear regression, and has been exposed to basic time series concepts as presented in Harvey (1993) or Franses (1998). Most importantly, the book assumes that the reader is interested in modeling and analyzing financial time series.
Overview of the Book
The chapters in the book cover univariate and multivariate models for analyzing financial time series using R. Chapter one gives a general overview of the use of R and highlights certain aspects of the language for statistical modeling. Chapter covers time series objects in R, especially the zoo and xts classes, and illustrates the specification, manipulation and visualization of these objects. Chapter three surveys time series concepts used throughout the book. Chapters four through eight cover a variety of topics in the modeling of univariate financial time series, including testing for unit roots, extreme value theory, time series regression models, GARCH models of volatility, and long memory models. Chapter nine introduces rolling analyses of time series models and covers related topics such as technical analysis of financial time series and moving average methods for high frequency data. Chapters ten through fifteen cover models for the analysis of multivariate financial time series. Topics include systems of regression equations, vector autoregressive models, cointegration, factor models, multivariate GARCH models, and state space models. Chapter 16 covers aspects of modeling time series arising from fixed income financial assets. These new chapters cover nonlinear regime-switching models, copulas, continuous-time models, the generalized method of moments.
About the Author
Eric Zivot is a the Roberts Richards Chaired Professor of Economics at the University of Washington and an Adjunct Professor in the Departments of Applied Mathematics, Finance, and Statistics. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He was an associate editor of the Journal of Business and Economic Statistics and Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, Econometrics Journal, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. He has also published papers in empirical finance journals including the Journal of Empirical Finance, the Journal of Financial Markets, and the Journal of International Money and Finance.
The authors may be contacted by electronic mail at
Last updated: May 15, 2014