Analysis of Financial Time Series (Wiley Series in Probability and Statistics)
Language: English
Published by John Wiley and Sons Ltd, United Kingdom, Chicester, 2005
Series: Book 195 of 358 - Wiley Series in Probability and Statistics
- Softcover
- Used
Seller: WorldofBooks, Goring-By-Sea, WS, United KingdomWorldofBooks
AbeBooks seller since March 16, 2007
Condition: Used - Very good
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Add to basketItem description from seller
This title 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; and, Bayesian inference in finance methods. This new edition is a thoroughly revised and updated text, including the addition of S-Plus(r) 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; and, 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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.…
Seller Inventory # GOR006747594
- Title
- Analysis of Financial Time Series (Wiley Series in Probability and Statistics)
- Author
- Ruey S. Tsay
- Publisher
- John Wiley and Sons Ltd, United Kingdom, Chicester
- Publication year
- 2005
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0471690740
- ISBN 13
- 9780471690740
- Edition
- 2nd Edition
- Item weight
- 989 grams
- Dimensions
- 164.0 x 238.0
- Series
- Book 195 of 358: 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 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.
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WorldofBooks
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