Provides statistical tools and techniques needed to understand today's financial markets The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will 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.
The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics:
* Analysis and application of univariate financial time series
* Return series of multiple assets
* Bayesian inference in finance methods
This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find:
* Consistent covariance estimation under heteroscedasticity and serial correlation
* Alternative approaches to volatility modeling
* Financial factor models
* State-space models
* Kalman filtering
* Estimation of stochastic diffusion models
The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.
Content: Chapter 1 Financial Time Series and Their Characteristics (pages 1–23): Chapter 2 Linear Time Series Analysis and Its Applications (pages 24–96): Chapter 3 Conditional Heteroscedastic Models (pages 97–153): Chapter 4 Nonlinear Models and Their Applications (pages 154–205): Chapter 5 High?Frequency Data Analysis and Market Microstructure (pages 206–250): Chapter 6 Continuous?Time Models and Their Applications (pages 251–286): Chapter 7 Extreme Values, Quantile Estimation, and Value at Risk (pages 287–338): Chapter 8 Multivariate Time Series Analysis and Its Applications (pages 339–404): Chapter 9 Principal Component Analysis and Factor Models (pages 405–442): Chapter 10 Multivariate Volatility Models and Their Applications (pages 443–489): Chapter 11 State?Space Models and Kalman Filter (pages 490–542): Chapter 12 Markov Chain Monte Carlo Methods with Applications (pages 543–600):

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Analysis of Financial Time Series (Wiley Series in Probability and Statistics)2nd edition

Analysis of financial time series : financial econometrics

Analysis of Financial Time Series, Second Edition

Ruey S Tsay, 1951-

Tsay, Ruey S.

Jossey-Bass, Incorporated Publishers

WILEY COMPUTING Publisher

John Wiley & Sons, Ltd.

Wiley-Interscience

Wiley series in probability and statistics, 2nd ed, Hoboken, N.J, 2005

Wiley Series in Probability and Statistics, 2005 aug 19

John Wiley & Sons, Inc., Hoboken, N.J., 2005

2nd ed., Hoboken, N.J, New Jersey, 2005

United States, United States of America

2., Auflage, New York, NY, 2005

New York, New York State, 2005

2nd ed, New York, N.Y, 2005

2 edition, August 26, 2005

August 30, 2005

2nd, PS, 2005

lg995659

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"Wiley-Interscience."
Includes bibliographical references and index.

Wiley Series in Probability and Statistics

The Second Edition Of This Critically Acclaimed Text Provides A Comprehensive And Systematic Introduction To Financial Econometric Models And Their Applications In Modeling And Predicting Financial Time Series Data. This Latest Edition Continues To Emphasize Empirical Financial Data And Focuses On Real-world Examples. Following This Approach, Readers Will 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. The Tools Provided In This Text Aid Readers In Developing A Deeper Understanding Of Financial Markets Through Firsthand Experience In Working With Financial Data. This Is An Ideal Textbook For Mba Students As Well As A Reference For Researchers And Professionals In Business And Finance.--jacket. 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. Ruey S. Tsay. Wiley-interscience. Includes Bibliographical References And Index.

Provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This edition emphasizes empirical financial data and focuses on real-world examples. It is useful for MBA students and for researchers and professionals in business and finance. Gain the statistical tools and techniques you need to understand today's financial markets with the Second Edition of this critically acclaimed book. You'll 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. You'll 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

This 2nd. ed. provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data, neural network applications, continous-time models amd Ito's Lemma, Value at Risk, financial factor models, and econometric modeling via computation-intensive methods

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

2013-08-01