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Analysis of Financial Time Series (Wiley Series in Probability and Statistics) | 
 enlarge | Author: Ruey S. Tsay
Publisher: Wiley-Interscience
Category: Book
List Price: $127.50
Buy New: $59.62
You Save: $67.88 (53%)
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Rating:  9 reviews
Sales Rank: 8381
Media: Hardcover
Edition: 2nd
Pages: 640
Number Of Items: 1
Shipping Weight (lbs): 2.2
Dimensions (in): 9.6 x 6.4 x 1.4
ISBN: 0471690740
Dewey Decimal Number: 332.0151955
EAN: 9780471690740
Publication Date: August 30, 2005
Availability: Usually ships in 1-2 business days
Shipping: Expedited shipping available
Condition: New Book, Hardcover. Same Edition As Amazon's Description! Never Been Read! Buy Now!
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Product Description
Gain the statistical tools and techniques you need to understand today's financial markets with the Second Edition of this critically acclaimed book. Youll find a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This edition continues to emphasize empirical financial data and focuses on real-world examples. Youll 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. This is an ideal textbook for MBA students and a key reference for researchers and professionals in business and finance. Order your copy today.
Download Description
Analysis of Financial Time Series, Second Edition provides a comprehensive and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: analysis and application of univariate financial time series; the return series of multiple assets; and Bayesian inference in finance methods.
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| Customer Reviews: Read 4 more reviews...
 Statistician's favorite December 10, 2004
hd (USA)
14 out of 16 found this review helpful
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.
Analysis of Financial Time Series October 17, 2003
10 out of 13 found this review helpful
This book is awesome. It starts with bedrock concepts needed for analysis of financial data and it takes the student up to the most recent and important techniques used in the industry today. However, if one expects to fully utilize this text, one should have at least one semester of applied econometrics or some equivalent course in statistics and continuous probability, although it will be practical to study the two topics concurrently.
Best textbook I have ever read September 19, 2005
Min Liu (TX USA)
7 out of 11 found this review helpful
First of all, it is well written in a very practical point of view. The whole book is aimed fullly to real financial data(appended in the author's web). People can gain not only the well-explained theories but the hand-on experience with data analysis using SPLUS or any other package.
Secondly, the author is a real expert in this field and has been publishing lots of nice work. All models in the book are clearly illustrated and commented.
Thirdly, it covers a lot of topics in analysis of FT. Reader can learn almost all the valuable things in this field from this book.
If anyone wanna truly learn this book, she/he has to sit down and plays some real data on computer. I think this is the best way and the only way to use this book.
The best for Masters level, great all-around February 11, 2007
GCC (NY)
2 out of 2 found this review helpful
This text is absolutely perfect for Masters students learning financial econometrics. There is a little theory, clear explanations, and quite a few real world examples. (I don't think any text would tell the reader what model to use when, because that's application-specific.) It assumes some knowledge of finance and basic econometrics/statistics, which is fair enough. To get more theory, Hamilton (1994) remains the authority, and Campbell, Lo, MacKinlay (1997) is a great introduction for PhD students, and generally an ideal companion volume to this one.
Excellent and detailed reference May 2, 2007
OmniReader (Lewisville, TX)
1 out of 1 found this review helpful
The coverage of the topic is broad and deep. It is one of the few introductory books that devotes some space to transfer function modeling and does so intelligibly.
A must have for the novice as well as those more familiar with the topic that need a solid reference.
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