- Hardcover: 416 pages
- Publisher: Wiley; 1 edition (October 29, 2012)
- Language: English
- ISBN-10: 0470890819
- ISBN-13: 978-0470890813
- Product Dimensions: 6.5 x 1.1 x 9.2 inches
- Shipping Weight: 1.5 pounds (View shipping rates and policies)
- Average Customer Review: 8 customer reviews
- Amazon Best Sellers Rank: #221,736 in Books (See Top 100 in Books)
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An Introduction to Analysis of Financial Data with R 1st Edition
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“I found this book highly informative and interesting to read. The proper mix of theory and hands-on programming examples makes it recommended reading for both R programmers interested in finance and financial analysts with a basic programming background. Well written and following a clear and defined logical layout, the author has written a current reference text on using a powerful open-source programming language for typical financial analysis.” (Computing Reviews, 25 March 2014)
“All in all, this book is a good and useful introduction to financial time series with many real-world examples. It is suitable for use both as a textbook and for self-study, with exercises provided at the end of each chapter.” (International Statistical Review, 14 June 2013)
From the Back Cover
A complete set of statistical tools for beginning financial analysts from a leading authority
Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research.
The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including:
- Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison
- Different approaches to calculating asset volatility and various volatility models
- High-frequency financial data and simple models for price changes, trading intensity, and realized volatility
- Quantitative methods for risk management, including value at risk and conditional value at risk
- Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression
Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques.
An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.
Top customer reviews
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What I found lacking in this book was more description of the application of the methods described. The presentation is: here is a method for analyzing a time series and here is some R code (sometimes). It assumes that the reader already understands the derivation/origin of the method being presented from a signal processing/mathematics standpoint. The text does a poor job of explaining the role of each method in financial modeling. How would one apply a given technique and exactly for what purpose? So, the book seems to be written for that narrow audience who knows the derivation of the methods presented and knows the application as well. Equations are presented with a terse explanation of their parameters.
Finally, the book cries out for a good editor. Dr. Tsay does not write clearly, unfortunately, and in many instances the syntax of the book is cumbersome, making the meaning difficult to discern.
The author has gone great lengths to include mathematical rigor in the book but the code is staring at me as early as page 13!!
Code R Demonstration with quantmod package on page 13 does not work.Someone has to spend a full day trying to figure out what the author left out..it's a painful experience.