Author_Details
Ruey S. Tsay, Ph.D., is H. G. R. Alexander Professor of Econometrics and Statistics in the Graduate School of Business at the University of Chicago. He is the 2003 recipient of the John Wiley & Sons, Inc. Award for "Excellence in Academic Writing".
About_Topic
Financial time series analysis is concerned with theory and practice of asset valuation over time. It is a highly empirical discipline, but like other scientific fields theory forms the foundation for making inference. There is, however, a key feature that distinguishes financial time series analysis from other time series analysis. Both financial theory and its empirical times series contain an element of uncertainty. For example, there are various definitions of asset volatility, and for a stock return series, the volatility is not directly observable. As a result of the added uncertainty, statistical theory and methods play an important role in financial time series analysis.
About_Book
This book 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. 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.
Main_Blurb
Short_Blurb
TOC
Preface.
Preface to First Edition.
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, Quantile Estimation, 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.
Index.
Concise_Desc
This book 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. 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.
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, Quantile Estimation, 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.
