Analysis of Financial Time Series (Wiley Series in Probability and Statistics, Band 762) - gebunden oder broschiert

EAN (ISBN-13): 9780470414354
ISBN (ISBN-10): 0470414359
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: Wiley
678 Seiten
Gewicht: 1,127 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2008-09-02T10:38:22+02:00 (Berlin)
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ISBN/EAN: 9780470414354

ISBN - alternative Schreibweisen:
0-470-41435-9, 978-0-470-41435-4
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: tsay
Titel des Buches: tsay, analysis financial time series, probability and statistics, little time, how tell time, time and the other, time was, just not time, time brief, within time, analysis band

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Daten vom Verlag:

Autor/in: Ruey S. Tsay
Titel: Analysis of Financial Time Series
Verlag: Wiley; John Wiley & Sons
712 Seiten
Erscheinungsjahr: 2010-09-10
Gewicht: 1,111 kg
Sprache: Englisch
155,00 € (DE)
Not available (reason unspecified)
164mm x 238mm x 41mm

BB; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Finance & Investments; Financial Engineering; Finanz- u. Anlagewesen; Finanz- u. Wirtschaftsstatistik; Finanztechnik; Finanzwirtschaft; Statistics; Statistics for Finance, Business & Economics; Statistik; Time Series; Zeitreihen; Zeitreihenanalyse; Finanztechnik; Finanz- u. Wirtschaftsstatistik; Zeitreihen; Ökonometrie und Wirtschaftsstatistik

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.