Download Analysis of Financial Time Series, by Ruey S. Tsay
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Analysis of Financial Time Series, by Ruey S. Tsay
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Fundamental topics and new methods in time series analysis
Analysis of Financial Time Series provides a comprehensive and systematic introduction to financial econometric models and their application 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. Timely topics and recent results include:
- Value at Risk (VaR)
- High-frequency financial data analysis
- Markov Chain Monte Carlo (MCMC) methods
- Derivative pricing using jump diffusion with closed-form formulas
- VaR calculation using extreme value theory based on a non-homogeneous two-dimensional Poisson process
- Multivariate volatility models with time-varying correlations
Ideal as a fundamental introduction to time series for MBA students or as a reference for researchers and practitioners in business and finance, Analysis of Financial Time Series offers an in-depth and up-to-date account of these vital methods.
- Sales Rank: #1278912 in Books
- Published on: 2001-10-15
- Original language: English
- Number of items: 1
- Dimensions: 9.43" h x 1.04" w x 6.28" l, 1.10 pounds
- Binding: Hardcover
- 472 pages
Most helpful customer reviews
44 of 46 people found the following review helpful.
Broad coverage, but not for the faint-hearted
By Gadgester
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.
33 of 38 people found the following review helpful.
good coverage
By Michael R. Chernick
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.
21 of 23 people found the following review helpful.
Statistician's favorite
By hd
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.
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