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.
Autorentext
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.
Klappentext
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.
Zusammenfassung
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.
Inhalt
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 BidAsk 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
…