Analysis of Financial Time Series (Wiley Series in Probability and Statistics) - Tapa dura

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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.

De la contraportada

Praise for the Second Edition

". . . too wonderful a book to be missed by anyone who works in time series analysis."
&;Journal of Statistical Computation and Simulation

"All in all this is an excellent account on financial time series...with plenty of intuitive insight of how exactly these models work..."
&;MAA Reviews

Since publication of the first edition, Analysis of Financial Time Series has served as one of the most influential and prominent works on the subject. This Third Edition now utilizes the freely available R software package to explore empirical financial data and illustrate related computation and analyses using real-world examples. Retaining the fundamental and hands-on style of its predecessor, this new edition continues to serve as the cornerstone for understanding the important statistical methods and techniques for working with financial data.

Accessible explanations and numerous interesting examples assist readers with understanding analysis and application of univariate financial time series; return series of multiple assets; and Bayesian inference in finance methods. The latest developments in financial econometrics are explored in-depth, such as realized volatility, volatility with skew innovations, conditional value at risk, statistical arbitrage, and applications of duration and dynamic-correlation models. Additional features of the Third Edition include:

• Applications of nonlinear duration models throughout all discussion of high-frequency data analysis and market microstructure

• Newly added applications of nonlinear models and methods

• An updated chapter on multivariate time series analysis that explores the relevance of cointegration to pairs trading

• A new, unified approach to value at risk (VaR) via loss function

• An introduction to extremal index for dependence data in the discussion of extreme values, quantiles, and value at risk

The use of both R and S-PLUS software with the book's numerous examples and exercises ensures that readers can reproduce the results shown in the book and apply the detailed steps and procedures to their own work. New and updated exercises throughout provide opportunities to test comprehension of the presented material, and a related Web site houses additional data sets and related software programs.

Analysis of Financial Time Series, Third Edition is an ideal book for introductory courses on time series at the graduate level and a valuable supplement for statistics courses in time series at the upper-undergraduate level. It also serves as an indispensible reference for researchers and practitioners working in business and finance.

De la solapa interior

Praise for the Second Edition

. . . too wonderful a book to be missed by anyone who works in time series analysis.
--Journal of Statistical Computation and Simulation

All in all this is an excellent account on financial time series...with plenty of intuitive insight of how exactly these models work...
--MAA Reviews

Since publication of the first edition, Analysis of Financial Time Series has served as one of the most influential and prominent works on the subject. This Third Edition now utilizes the freely available R software package to explore empirical financial data and illustrate related computation and analyses using real-world examples. Retaining the fundamental and hands-on style of its predecessor, this new edition continues to serve as the cornerstone for understanding the important statistical methods and techniques for working with financial data.

Accessible explanations and numerous interesting examples assist readers with understanding analysis and application of univariate financial time series; return series of multiple assets; and Bayesian inference in finance methods. The latest developments in financial econometrics are explored in-depth, such as realized volatility, volatility with skew innovations, conditional value at risk, statistical arbitrage, and applications of duration and dynamic-correlation models. Additional features of the Third Edition include:

• Applications of nonlinear duration models throughout all discussion of high-frequency data analysis and market microstructure

• Newly added applications of nonlinear models and methods

• An updated chapter on multivariate time series analysis that explores the relevance of cointegration to pairs trading

• A new, unified approach to value at risk (VaR) via loss function

• An introduction to extremal index for dependence data in the discussion of extreme values, quantiles, and value at risk

The use of both R and S-PLUS software with the book's numerous examples and exercises ensures that readers can reproduce the results shown in the book and apply the detailed steps and procedures to their own work. New and updated exercises throughout provide opportunities to test comprehension of the presented material, and a related Web site houses additional data sets and related software programs.

Analysis of Financial Time Series, Third Edition is an ideal book for introductory courses on time series at the graduate level and a valuable supplement for statistics courses in time series at the upper-undergraduate level. It also serves as an indispensible reference for researchers and practitioners working in business and finance.