Tsay Analysis of Financial Time Series

3. Auflage 2010
ISBN: 978-0-470-64455-3
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 720 Seiten, E-Book

This book provides a broad, mature, and systematic introduction tocurrent financial econometric models and their applications tomodeling and prediction of financial time series data. It utilizesreal-world examples and real financial data throughout the book toapply the models and methods described.
The author begins with basic characteristics of financial timeseries data before covering three main topics:
* Analysis and application of univariate financial timeseries
* The return series of multiple assets
* Bayesian inference in finance methods
Key features of the new edition include additional coverage ofmodern day topics such as arbitrage, pair trading, realizedvolatility, and credit risk modeling; a smooth transition fromS-Plus to R; and expanded empirical financial data sets.
The overall objective of the book is to provide some knowledgeof financial time series, introduce some statistical tools usefulfor analyzing these series and gain experience in financialapplications of various econometric methods.

Autoren/Hrsg.

Tsay, Ruey S.

Inhaltsverzeichnis

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 Dimensional Volatility Models.
10.6 Factor-Volatility Models.
10.7 Application.
10.8 Multivariate t Distribution.
11 State-Space Models and Kalman Filter.
11.1 Local Trend Model.
11.2 Linear State-Space Models.
11.3 Model Transformation.
11.4 Kalman Filter and Smoothing.
11.5 Missing Values.
11.6 Forecasting.
11.7 Application.
12 Markov Chain Monte Carlo Methods with Applications.
12.1 Markov Chain Simulation.
12.2 Gibbs Sampling.
12.3 Bayesian Inference.
12.4 Alternative Algorithm.
12.5 Linear Regression With Time Series Errors.
12.6 Missing Values and Outliers.
12.7 Stochastic Volatility Models.
12.8 A New Approach to SV Estimation.
12.9 Markov Switching Models.
12.10 Forecasting.
12.11 Other Applications.

Über Autor(innen)

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.