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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..."
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.Revue de presse:
"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." ( Mathematical Reviews, 2011)
"Nevertheless, all in all the book can be a very useful reference for students as well as for professionals." (Zentralblatt MATH, 2011)
"Factor models, an important technique used in quantitative finance, are given a full treatment with macroeconomic factor models and fundamental factor models.
The coverage of the book is comprehensive. It starts from basic time series techniques and finishes with advanced concepts such as state space models and MCMC methods. There is a balance between the theoretical background necessary to appreciate the nuances and the practical aspect of implementation. More importantly it gives insights about what time series models can′t address. The book has an excellent supporting website which has all the programs and data sets which helps to internalize the concepts. Finally, teaching professionals should find the solutions manual as a valuable tool to explain concepts and to ensure understanding." (BookPleasures.com, January 2011)
"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." (Insurance News Net, 8 December 2010)
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