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Informationen zum Autor 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 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 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. Zusammenfassung Analysis of Financial Time Series, Third Edition 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. Inhaltsverzeichnis 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 Model...
