Analysis of Financial Time Series 3rd Edition by Ruey S. Tsay (PDF)

Description

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. The author begins with basic characteristics of financial time series data before covering three main topics:Analysis and application of univariate financial time seriesThe return series of multiple assetsBayesian inference in finance methodsKey features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.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.

User’s Reviews

Editorial Reviews: Review “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) From the Inside Flap 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 ReviewsSince 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 microstructureNewly added applications of nonlinear models and methodsAn updated chapter on multivariate time series analysis that explores the relevance of cointegration to pairs tradingA new, unified approach to value at risk (VaR) via loss functionAn introduction to extremal index for dependence data in the discussion of extreme values, quantiles, and value at riskThe 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. From the Back Cover 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 ReviewsSince 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 microstructureNewly added applications of nonlinear models and methodsAn updated chapter on multivariate time series analysis that explores the relevance of cointegration to pairs tradingA new, unified approach to value at risk (VaR) via loss functionAn introduction to extremal index for dependence data in the discussion of extreme values, quantiles, and value at riskThe 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. About the Author 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. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Tsay does an outstanding job taking commonly taught time series concepts and explaining them in a different way. He starts with review of the basics i.e., distributions, moments, processes, stationarity. He covers the functional form and properties of AR, MA, and ARMA models, as well as non-stationary processes (random walk), trend-stationary processes, unit-root tests (Dickey-Fuller), autocorrelation tests (Ljung Box), and seasonality. He naturally transitions into conditional hereoskedasticity models (ARCH/GARCH) and the many special cases (EGARCH/IGARCH/TGARCH/Stochastic vol.). He has a great section on nonlinear models, focusing on threshold and smooth transition AR models, Markov switching models, non-parametric models (kernel regression), and state-space models.His section on big/high-frequency data and market microstructure should be required reading. He covers nonsynchronous trading (and how it may induce serial or cross-correlation), bid-ask bounce, duration models, and the Eps effect.The section on continuous-time stochastic processes is extremely clear (more than other texts). It explains the Wiener process, Ito process (and Ito’s lemma), the Black Scholes Merton differential equation, the Black Scholes Merton pricing formula (risk-neutral proof), and jump-diffusion models.Tsay also includes an entire section on Value at Risk (parametric, historical, econometric), extreme value theory (Generalized Pareto and Peaks-over-thresholds), and the extremal index.Multivariate topics are covered, including vector autoregression (with Cholesky regularization), vector moving average, vector ARMA, cointegration, cointegrated VAR, vector error correction, and threshold cointegration. Multivariate volatility models are also mentioned, including multivariate GARCH and BEKK model.He explains principal component analysis in connection with factor models, including the Barra factor model and the Fama-French model. He also goes deeper into state space models, giving a full explanation of the local trend model and deriving the Kalman filter (as well as smoothing algorithms). At the end of the text, he has some advanced approaches to dealing with missing values and outliers, giving a brief overview of Bayesian inference and Markov chain Monte Carlo (Gibbs, Metropolis-Hastings, Griddy gibbs, etc.). He includes Monte Carlo applications, such as Markov switching and stochastic volatility models.

⭐A nice up-to-date collection of time series techniques. Should be useful for someone who already has some experience in the field. However, would not recommend as an introduction for an uninitiated.Pros:* Covers a broad scope of up-to-date time series topics.* Presentations of models are concise but not too short.Cons:* A bit uncomfortable and unconventional (compared to other time series texts) notation.* On some occasions presents outdated approaches that have been proved “wrong” without even giving a warning. E.g. suggests testing for remaining ARCH effects in (G)ARCH model residuals by the simple ARCH-LM test. This approach has been proven wrong and an alternative has been suggested already in 1994 (the Li-Mak test). However, the book is completely mute about this issue.* A few typos, but not a major problem.

⭐It’s a textbook.

⭐I realize a different reviewer considered the e-book a convenient solution, and it certainly would be. But, the equations are unreadable. You have to ‘double click’ with your finger to enlarge the equation, it’s barely readable at that. The inline equations are in fonts that don’t display, so there are missing subscripts. The result is, by Chapter 2 the text devolves into gibberish. Honestly, the quality control is lousy for this Kindle Edition. I guess I’ll have to buy the hardcover.As for the book, I’m through Chapter Three and it is otherwise readable and understandable. The author presents the material well, but I’ve already had one course on time series analysis.

⭐Very well explained book. Also attached branch of examples with R studio codes.

⭐This is a great time series’ book. It tries to comprise the most popular financial time series models used in finance.

⭐This book is not for you if you are just starting learn about Time Series analysis or Econometrics. This is way too math heavy and absolutely no attempt made to explain the concepts.

⭐It is great quality.

⭐Personally, I think this book is not for the beginners. If you are looking for a starter, this is not for you. This however does not devalue the quality of the book. It is expensive (I paid £50.80) but worth it as it brilliantly covers useful econometric/statistical tools, which are widely used in the financial services industry even today. A basic understanding of Mathematical Statistics and Finance at least at the UG level, if not the PG level will be very helpful to fully grasp and understand the book. Furthermore, background readings in (1) understanding MCMC, (2) Gibbs Algo, (3) Bayesian Statistics and (4) probability distribution would greatly enhance your understanding and appreciation of this book.In summary, this is a good book for researchers and junior and advanced practitioners alike.

⭐If you’re good in equation then read it and it is advanced book on econometrics.

⭐The ultimate in financial time series. Nothing better than this.

⭐Nice Book

⭐Great book but very technical.

Keywords

Free Download Analysis of Financial Time Series 3rd Edition in PDF format
Analysis of Financial Time Series 3rd Edition PDF Free Download
Download Analysis of Financial Time Series 3rd Edition 2010 PDF Free
Analysis of Financial Time Series 3rd Edition 2010 PDF Free Download
Download Analysis of Financial Time Series 3rd Edition PDF
Free Download Ebook Analysis of Financial Time Series 3rd Edition