Contents
Preface xvii
Preface to First Edition xix
Preface
The subject of financial time series analysis has attracted substantial attention in
recent years, especially with the 2003 Nobel awards to Professors Robert Engle and
Clive Granger. At the same time, the field of financial econometrics has undergone
various new developments, especially in high-frequency finance, stochastic volatility,
and software availability. There is a need to make the material more complete
and accessible for advanced undergraduate and graduate students, practitioners, and
researchers. The main goals in preparing this second edition have been to bring the
book up to date both in new developments and empirical analysis, and to enlarge
the core material of the book by including consistent covariance estimation under
heteroscedasticity and serial correlation, alternative approaches to volatility modeling,
financial factor models, state-space models, Kalman filtering, and estimation
of stochastic diffusion models.
The book therefore has been extended to 10 chapters and substantially revised
to include S-Plus commands and illustrations. Many empirical demonstrations and
exercises are updated so that they include the most recent data.
The two new chapters are Chapter 9, Principal Component Analysis and Factor
Models, and Chapter 11, State-Space Models and Kalman Filter. The factor models
discussed include macroeconomic, fundamental, and statistical factor models.
They are simple and powerful tools for analyzing high-dimensional financial data
such as portfolio returns. Empirical examples are used to demonstrate the applications.
The state-space model and Kalman filter are added to demonstrate their
applicability in finance and ease in computation. They are used in Chapter 12 to
estimate stochastic volatility models under the general Markov chain Monte Carlo
(MCMC) framework. The estimation also uses the technique of forward filtering
and backward sampling to gain computational efficiency.
A brief summary of the added material in the second edition is:
1. To update the data used throughout the book.
2. To provide S-Plus commands and demonstrations.
3. To consider unit-root tests and methods for consistent estimation of the
covariance matrix in the presence of conditional heteroscedasticity and serial
correlation in Chapter 2.
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xviii PREFACE
4. To describe alternative approaches to volatility modeling, including use of
high-frequency transactions data and daily high and low prices of an asset in
Chapter 3.
5. To give more applications of nonlinear models and methods in Chapter 4.
6. To introduce additional concepts and applications of value at risk in Chapter 7.
7. To discuss cointegrated vector AR models in Chapter 8.
8. To cover various multivariate volatility models in Chapter 10.
9. To add an effective MCMC method for estimating stochastic volatility models
in Chapter 12.
The revision benefits greatly from constructive comments of colleagues, friends,
and many readers on the first edition. I am indebted to them all. In particular, I
thank J. C. Artigas, Spencer Graves, Chung-Ming Kuan, Henry Lin, Daniel Pe˜na,
Jeff Russell, Michael Steele, George Tiao, Mark Wohar, Eric Zivot, and students
of my MBA classes on financial time series for their comments and discussions,
and Rosalyn Farkas, production editor, at John Wiley. I also thank my wife and
children for their unconditional support and encouragement. Part of my research in
financial econometrics is supported by the National Science Foundation, the High-
Frequency Finance Project of the Institute of Economics, Academia Sinica, and the
Graduate School of Business, University of Chicago.
Finally, the website for the book is:
gsbwww.uchicago.edu/fac/ruey.tsay/teaching/fts2.
Ruey S. Tsay
University of Chicago
Chicago, Illinois