Product Description

Provides statistical tools and techniques needed to understand today's financial markets

The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.

The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics:

• Analysis and application of univariate financial time series
• Return series of multiple assets
• Bayesian inference in finance methods

This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find:

• Consistent covariance estimation under heteroscedasticity and serial correlation
• Alternative approaches to volatility modeling
• Financial factor models
• State-space models
• Kalman filtering
• Estimation of stochastic diffusion models

The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.

Product Details

• Amazon Sales Rank: #35348 in Books
• Published on: 2005-08-30
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 640 pages

Review
"…too wonderful [a] book to be missed by any one who works in time series analysis." (Journal of Statistical Computation and Simulation, October 2006)

"...an excellent account of financial time series...[for] students and especially to practitioners, who really need a book with enough...theoretical concepts...but also with plenty of intuitive insight of how exactly these models work…" (MAA Reviews, January 2, 2006)

Review
“…in my view, this is the number one reference for a course on financial econometrics...” (Statistical Papers, Vol.45, No.4, October 2004)

“…covers classical and new topics of financial econometrics…lots of examples, exercises and references at each chapter…” (Zentralblatt Math, Vol.1037, No.12, 2004)

"A textbook for graduate students of business or of mathematics with a business orientation." (Reference & Research Book News, May 2002)

"...an introductory book intended to provide a comprehensive and systematic account of financial econometric models and their application to modeling and prediciont..." (Quarterly of Applied Mathematics, Vol. LX, No. 2, June 2002)

"...an insightful and timely text…compelling reading...I would strongly consider using this text.." (Journal of Financial Research, Fall 2002)

"Always looking for a newer and better book, I will certainly enjoy having Analysis of Financial Time Series as my new primary resource." (Technometrics, Vol. 44, No. 4, November 2002)

From the Back Cover
Provides statistical tools and techniques needed to understand today's financial markets

The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.

The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics:

• Analysis and application of univariate financial time series
• Return series of multiple assets
• Bayesian inference in finance methods

This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find:

• Consistent covariance estimation under heteroscedasticity and serial correlation
• Alternative approaches to volatility modeling
• Financial factor models
• State-space models
• Kalman filtering
• Estimation of stochastic diffusion models

The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.

Customer Reviews

Broad coverage, but not for the faint-hearted
Written by a University of Chicago professor, this book comprehensively covers times series topics relative to investment and trading-oriented finance (i.e., Wall Street money-making machines). Treatment is generally clear and thorough, but an advanced math and stat background is an absolute prerequisite for understanding the materials.

S-Plus/R code is given, but strangely, there is very little on *why* and
*when* one uses each of the techniques. Under what cirmcustances should I use or not use GARCH? What exactly is PCA good for in real-world applications? These important questions are not answered, in other words, you don't get a sense of the real-world context for these topics.

good coverage
Professor Tsay is a student of the Wisconsin school of statisticians where he learned time series from Box and Tiao. He is an excellent lecturer and a good writer. I have attended one of the short courses he taught on time series. New models have been developed to deal with the special behavior of financial time series. Professor Tsay is always at the forefront of that research and teaches at Chicago in one of this country's top business schools. If I am correct George Tiao is also there at present.

This is the second edition of a popular text. Financial time series play an ever more important role in our lives during these turbulant economic times. Tsay cover the tradition Box-Jenkins models but these models are not always appropriate for financial data. So he also introduces the GARCH models and some nonlinear models. The book includes some models that I am not familiar with. I have done research in time series but never with financial data. There is some theory involving stochastic differential equations that explains some of the turbulant behavior of financial series. The text by J. Michael Steele provides thorough coverage to this theory.

Tsay also deals with the pesky problem of outliers. A very practical problem that is often ignored in other econometric texts. He also has a chapter on Bayesian approaches. Some computing in SPlus is also included in this revision of the text.

Statistician's favorite
I had a detailed study of the whole book before finally deciding to buy in on web. As a statistician and a beginner on Math Finance, I would say this book deserves every penny I spent on it.

The author's intention to make it a reference book can be appreciated by both educators and practitioners. It starts with a couple of chapters on the ARIMA and the GARCH models. Little theoretic depth was explored yet the algorithms and the procedures for solution are emphasized. After that, the topic switches to the nonlinear time series modeling and high-freq data analysis. This part is, and will be, rather confusing to readers with less training in financial economics and theories are reluctantly clearly stated. What follows is a single chapter of so-called continuous time models and it is actually a sketch of the first few chapters of any mathematical finance textbook. Literally, this chapter is all about Black-Scholes and a little jump-diffusion model. The major reason why I called it a reference book is because it includes one chapter on VaR between the math finance chapter and the multi-variate time series models part. The author didn't say much more than that VaR is essentially some quantile calculation, which is fine in the statistical meaning. However, this description seems really "shallow" as compared with Jorion's book on VaR and risk management.

After all, I would give it a five star because its comprehensiveness and the author's effort to incorporate so many things in order to re-define the framework of financial time series analysis.