This book provides a broad, mature, 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
• Bayesian inference in finance methods

Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.

The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

Table of Contents

1 Financial Time Series and Their Characteristics

2 Linear Time Series Analysis and Its Applications

3 Conditional Heteroscedastic Models

4 Nonlinear Models and Their Applications

5 High-Frequency Data Analysis and Market Microstructure

6 Continuous-Time Models and Their Applications

7 Extreme Values, Quantiles, and Value at Risk

8 Multivariate Time Series Analysis and Its Applications

9 Principal Component Analysis and Factor Models

10 Multivariate Volatility Models and Their Applications

11 State-Space Models and Kalman Filter

12 Markov Chain Monte Carlo Methods with Applications

About the Author

Ruey S. Tsay, PhD, is H. G. B. Alexander Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Dr. Tsay has written over 100 published articles in the areas of business and economic forecasting, data analysis, risk management, and process control, and he is the coauthor of A Course in Time Series Analysis (Wiley). Dr. Tsay is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the Royal Statistical Society, and Academia Sinica.