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GARCH models : structure, statistical inference, and financial applications / Christian Francq; Jean-Michel Zakoian
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Original language: French Publisher: Chichester, West Sussex : Wiley, 2010Edition: Online-AusgDescription: Online-Ressource (1 online resource (xiv, 489 p.)) : illISBN:- 9781282794511
- 1282794515
- 9780470670040
- Modèles GARCH <English>
- Francq, Christian. Modèles GARCH <English>
- Francq, Christian. Modèles GARCH. <engl.>
- 332.015195
- 332.01/5195
- HG106
Contents:
Summary: This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.Summary: Intro -- GARCH Models -- Contents -- Preface -- Notation -- 1 Classical Time Series Models and Financial Series -- 1.1 Stationary Processes -- 1.2 ARMA and ARIMA Models -- 1.3 Financial Series -- 1.4 Random Variance Models -- 1.5 Bibliographical Notes -- 1.6 Exercises -- Part I Univariate GARCH Models -- 2 GARCH(p, q) Processes -- 2.1 Definitions and Representations -- 2.2 Stationarity Study -- 2.2.1 The GARCH(1, 1) Case -- 2.2.2 The General Case -- 2.3 ARCH (¡Ä) Representation. -- 2.3.1 Existence Conditions -- 2.3.2 ARCH (¡Ä) Representation of a GARCH -- 2.3.3 Long-Memory ARCH -- 2.4 Properties of the Marginal Distribution -- 2.4.1 Even-Order Moments -- 2.4.2 Kurtosis -- 2.5 Autocovariances of the Squares of a GARCH -- 2.5.1 Positivity of the Autocovariances -- 2.5.2 The Autocovariances Do Not Always Decrease -- 2.5.3 Explicit Computation of the Autocovariances of the Squares -- 2.6 Theoretical Predictions -- 2.7 Bibliographical Notes -- 2.8 Exercises -- 3 Mixing* -- 3.1 Markov Chains with Continuous State Space -- 3.2 Mixing Properties of GARCH Processes -- 3.3 Bibliographical Notes -- 3.4 Exercises -- 4 Temporal Aggregation and Weak GARCH Models -- 4.1 Temporal Aggregation of GARCH Processes -- 4.1.1 Nontemporal Aggregation of Strong Models -- 4.1.2 Nonaggregation in the Class of Semi-Strong GARCH Processes -- 4.2 Weak GARCH -- 4.3 Aggregation of Strong GARCH Processes in the Weak GARCH Class -- 4.4 Bibliographical Notes -- 4.5 Exercises -- Part II Statistical Inference -- 5 Identification -- 5.1 Autocorrelation Check for White Noise -- 5.1.1 Behavior of the Sample Autocorrelations of a GARCH Process -- 5.1.2 Portmanteau Tests -- 5.1.3 Sample Partial Autocorrelations of a GARCH -- 5.1.4 Numerical Illustrations -- 5.2 Identifying the ARMA Orders of an ARMA-GARCH -- 5.2.1 Sample Autocorrelations of an ARMA-GARCH.PPN: PPN: 809065495Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PBE | ZDB-30-PQE
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