Analysis of Financial Time Series

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 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. 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<...

List of contents

1 Financial Time Series and Their Characteristics.
 
1.1 Asset Returns.
 
1.2 Distributional Properties of Returns.
 
1.3 Processes Considered.
 
2 Linear time series.
 
2.1 Stationarity.
 
2.2 Autocorrelation.
 
2.3 Linear time series.
 
2.4 Simple AR models.
 
2.5 Simple MA models.
 
2.6 Simple ARMA Models.
 
2.7 Unit-Root Nonstationarity.
 
2.8 Seasonal Models.
 
2.9 Regression with Correlated Errors.
 
2.10 Consistent Covariance Matrix Estimation.
 
2.11 Long-Memory Models.
 
3 Volatility models.
 
3.1 Characteristics of Volatility.
 
3.2 Structure of a Model.
 
3.3 Model Building.
 
3.3.1 Testing for ARCH Effect.
 
3.4 The ARCH Model.
 
3.5 The GARCH Model.
 
3.6 The Integrated GARCH Model.
 
3.7 The GARCH-M Model.
 
3.8 The Exponential GARCH Model.
 
3.9 The Threshold GARCH Model.
 
3.10 The CHARMA Model.
 
3.11 Random Coefficient Autoregressive Models.
 
3.12 The Stochastic Volatility Model.
 
3.13 The Long-Memory Stochastic Volatility Model.
 
3.14 Application.
 
3.15 Alternative Approaches.
 
3.16 Kurtosis of GARCH Models.
 
4 Nonlinear Models and Their Applications.
 
4.1 Nonlinear Models.
 
4.3 Modeling.
 
4.4 Forecasting.
 
4.5 Application.
 
5 High-Frequency Data Analysis and Market Microstructure.
 
5.1 Nonsynchronous Trading.
 
5.2 Bid-Ask Spread.
 
5.3 Empirical Characteristics of Transactions Data.
 
5.4 Models for Price Changes.
 
5.5 Duration Models.
 
5.6 Nonlinear Duration Models.
 
5.7 Bivariate Models for Price Change and Duration.
 
5.8 Application.
 
6 Continuous-Time Models and Their Applications.
 
6.1 Options.
 
6.2 Some Continuous-Time Stochastic Processes.
 
6.3 Ito's Lemma.
 
6.4 Distributions of Price and Return.
 
6.5 Black-Scholes Equation.
 
6.6 Black-Scholes Pricing Formulas.
 
6.7 An Extension of Ito's Lemma.
 
6.8 Stochastic Integral.
 
6.9 Jump Diffusion Models.
 
6.10 Estimation of Continuous-Time Models.
 
7 Extreme Values, Quantiles, and Value at Risk.
 
7.1 Value at Risk.
 
7.2 RiskMetrics.
 
7.3 An Econometric Approach to VaR Calculation.
 
7.4 Quantile Estimation.
 
7.5 Extreme Value Theory.
 
7.6 Extreme Value Approach to VaR.
 
7.7 A New Approach to VaR.
 
7.8 The Extremal Index.
 
8 Multivariate Time Series Analysis and Its Applications.
 
8.1 Weak Stationarity and Cross-Correlation Matrices.
 
8.2 Vector Autoregressive Models.
 
8.3 Vector Moving-Average Models.
 
8.4 Vector ARMA Models.
 
8.5 Unit-Root Nonstationarity and Cointegration.
 
8.6 Cointegrated VAR Models.
 
8.7 Threshold Cointegration and Arbitrage.
 
8.8 Pairs Trading.
 
9 Principal Component Analysis and Factor Models.
 
9.1 A Factor Model.
 
9.2 Macroeconometric Factor Models.
 
9.3 Fundamental Factor Models.
 
9.4 Principal Component Analysis.
 
9.5 Statistical Factor Analysis.
 
9.6 Asymptotic Principal Component Analysis.
 
10 Multivariate Volatility Models and Their Applications.
 
10.1 Exponentially Weighted Estimate.
 
10.2 Some Multivariate GARCH Models.
 
10.3 Reparameterization.
 
10.4 GARCH Models for Bivariate Returns.
 
10.5 Higher Dimension

Report

"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)

Product details

• Authors: RS Tsay, Ruey S Tsay, Ruey S. Tsay, Ruey S. (University of Chicago Tsay, Tsay Ruey S.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 10.09.2010
• EAN: 9780470414354
• ISBN: 978-0-470-41435-4
• No. of pages: 678
• Dimensions: 164 mm x 240 mm x 40 mm
• Series: Wiley Series in Probability and Statistics; Wiley Desktop Editions; Soziale Arbeit - Ethik - Religion; Wiley Series in Probability and Statistics; CourseSmart; Wiley Series in Probability an; Soziale Arbeit - Ethik - Religion
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Business; Statistik, Finanzwirtschaft, Finanzmathematik, Statistics, Zeitreihen, Zeitreihenanalyse, Financial Engineering, Finance & Investments, Finanz- u. Anlagewesen, Time Series, Statistics for Finance, Business & Economics