Analysis of Financial Time Series
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Analysis of Financial Time Series

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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 series * The return series of multiple assets * Bayesian inference in finance methods Key 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.
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Product details

  • Hardback | 720 pages
  • 165 x 237 x 43mm | 1,180g
  • New York, United States
  • English
  • 3rd Edition
  • 0470414359
  • 9780470414354
  • 127,492

Back cover copy

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.
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Table of contents

Preface xvii Preface to the Second Edition xix Preface to the First Edition xxi 1 Financial Time Series and Their Characteristics 1 1.1 Asset Returns, 2 1.2 Distributional Properties of Returns, 7 1.3 Processes Considered, 22 2 Linear Time Series Analysis and Its Applications 29 2.1 Stationarity, 30 2.2 Correlation and Autocorrelation Function, 30 2.3 White Noise and Linear Time Series, 36 2.4 Simple AR Models, 37 2.5 Simple MA Models, 57 2.6 Simple ARMA Models, 64 2.7 Unit-Root Nonstationarity, 71 2.8 Seasonal Models, 81 2.9 Regression Models with Time Series Errors, 90 2.10 Consistent Covariance Matrix Estimation, 97 2.11 Long-Memory Models, 101 3 Conditional Heteroscedastic Models 109 3.1 Characteristics of Volatility, 110 3.2 Structure of a Model, 111 3.3 Model Building, 113 3.4 The ARCH Model, 115 3.5 The GARCH Model, 131 3.6 The Integrated GARCH Model, 140 3.7 The GARCH-M Model, 142 3.8 The Exponential GARCH Model, 143 3.9 The Threshold GARCH Model, 149 3.10 The CHARMA Model, 150 3.11 Random Coefficient Autoregressive Models, 152 3.12 Stochastic Volatility Model, 153 3.13 Long-Memory Stochastic Volatility Model, 154 3.14 Application, 155 3.15 Alternative Approaches, 159 3.16 Kurtosis of GARCH Models, 165 4 Nonlinear Models and Their Applications 175 4.1 Nonlinear Models, 177 4.2 Nonlinearity Tests, 205 4.3 Modeling, 214 4.4 Forecasting, 215 4.5 Application, 218 5 High-Frequency Data Analysis and Market Microstructure 231 5.1 Nonsynchronous Trading, 232 5.2 Bid-Ask Spread, 235 5.3 Empirical Characteristics of Transactions Data, 237 5.4 Models for Price Changes, 244 5.5 Duration Models, 253 5.6 Nonlinear Duration Models, 264 5.7 Bivariate Models for Price Change and Duration, 265 5.8 Application, 270 6 Continuous-Time Models and Their Applications 287 6.1 Options, 288 6.2 Some Continuous-Time Stochastic Processes, 288 6.3 Ito's Lemma, 292 6.4 Distributions of Stock Prices and Log Returns, 297 6.5 Derivation of Black-Scholes Differential Equation, 298 6.6 Black-Scholes Pricing Formulas, 300 6.7 Extension of Ito's Lemma, 309 6.8 Stochastic Integral, 310 6.9 Jump Diffusion Models, 311 6.10 Estimation of Continuous-Time Models, 318 7 Extreme Values, Quantiles, and Value at Risk 325 7.1 Value at Risk, 326 7.2 RiskMetrics, 328 7.3 Econometric Approach to VaR Calculation, 333 7.4 Quantile Estimation, 338 7.5 Extreme Value Theory, 342 7.6 Extreme Value Approach to VaR, 353 7.7 New Approach Based on the Extreme Value Theory, 359 7.8 The Extremal Index, 377 8 Multivariate Time Series Analysis and Its Applications 389 8.1 Weak Stationarity and Cross-Correlation Matrices, 390 8.2 Vector Autoregressive Models, 399 8.3 Vector Moving-Average Models, 417 8.4 Vector ARMA Models, 422 8.5 Unit-Root Nonstationarity and Cointegration, 428 8.6 Cointegrated VAR Models, 432 8.7 Threshold Cointegration and Arbitrage, 442 8.8 Pairs Trading, 446 9 Principal Component Analysis and Factor Models 467 9.1 A Factor Model, 468 9.2 Macroeconometric Factor Models, 470 9.3 Fundamental Factor Models, 476 9.4 Principal Component Analysis, 483 9.5 Statistical Factor Analysis, 489 9.6 Asymptotic Principal Component Analysis, 498 10 Multivariate Volatility Models and Their Applications 505 10.1 Exponentially Weighted Estimate, 506 10.2 Some Multivariate GARCH Models, 510 10.3 Reparameterization, 516 10.4 GARCH Models for Bivariate Returns, 521 10.5 Higher Dimensional Volatility Models, 537 10.6 Factor-Volatility Models, 543 10.7 Application, 546 10.8 Multivariate t Distribution, 548 11 State-Space Models and Kalman Filter 557 11.1 Local Trend Model, 558 11.2 Linear State-Space Models, 576 11.3 Model Transformation, 577 11.4 Kalman Filter and Smoothing, 591 11.5 Missing Values, 600 11.6 Forecasting, 601 11.7 Application, 602 12 Markov Chain Monte Carlo Methods with Applications 613 12.1 Markov Chain Simulation, 614 12.2 Gibbs Sampling, 615 12.3 Bayesian Inference, 617 12.4 Alternative Algorithms, 622 12.5 Linear Regression with Time Series Errors, 624 12.6 Missing Values and Outliers, 628 12.7 Stochastic Volatility Models, 636 12.8 New Approach to SV Estimation, 649 12.9 Markov Switching Models, 660 12.10 Forecasting, 666 12.11 Other Applications, 669 Exercises, 670 References, 671 Index 673
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Review Text

"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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Review quote

"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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About Ruey S. Tsay

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.
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Rating details

79 ratings
4.21 out of 5 stars
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4 43% (34)
3 11% (9)
2 3% (2)
1 1% (1)
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