Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet.
Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data.
* Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions.
* Describes both the basics of R and advanced techniques useful in financial data analysis.
* Driven by real, current financial data, not just stale data deposited on some static website.
* Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.
Table of Contents
1. The Nature of Financial Data. 2. Sources of Financial Data and Software to Work with It. 3. Statistical Analysis of Financial Data. 4. Time Series Analysis. 5. Nonparametric Smoothing and Pattern Recognition. 6. Portfolios of Assets 7. Futures and Derivatives.
James E. Gentle is University Professor Emeritus at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra.
"The book is very well written, and fills an important need for an up-to-date textbook about statistical techniques applied to finance. The book explains the theory behind the statistical techniques very well, with good detail. The mathematical notation is appealing and elegant."
~Jerzy Pawlowski, New York University Tandon School of Engineering
"I thoroughly enjoyed reading the first two chapters of the book. Often, the first couple of chapters of a book provide a "boilerplate" discussion of the characteristics of the data and R. Here, the first two chapters are very well developed, to the point that they provide a good general resource to readers approaching the analysis of financial data from several different perspectives. For example, students in statistics usually approach the entire analysis of time series having in mind the potential application to the analysis of financial data, but they know nothing about the characteristics of the data and the financial markets...Just like the previous chapters, I broadly enjoyed reading this chapter. Prof. Gentle explains the topics clearly and often uses simulations to convey the intuition. That's also the way I like to teach these concepts and I think it enhances understanding among economics and finance students. I also commend the way he discusses the lag and difference operators and how they are implemented in R. He devotes quite some space to them, and I believe that is good as many texts go over these concepts too quickly for many students. Likewise, the discussion of the AR(I)MA models is very detailed and clear.
~Jan Annaert, University of Antwerp and Antwerp Management School