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Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline
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 new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used.
GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references.
GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
À propos de l?auteur:
CHRISTIAN FRANCQ, PHD, is Professor of Econometrics and Finance at CREST (Center for Research in Economics and Statistics) and ENSAE (National School of Statistics and Economic Administration).
JEAN-MICHEL ZAKOIAN, PHD, is Professor of Econometrics and Finance at CREST (Center for Research in Economics and Statistics) and ENSAE (National School of Statistics and Economic Administration).
They have both published various papers on this topic in statistical and econometric journals, including Econometrica, Econometric Theory, Journal of Econometrics, Bernoulli, Journal of the Royal Statistical Society (Series B) and Journal of the American Statistical Association.
Titre : GARCH Models: Structure, Statistical ...
Éditeur : Wiley
Date d'édition : 2019
Reliure : Couverture rigide
Etat : New
Edition : 2ème Édition
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Hardcover. Etat : new. Hardcover. Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline 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 new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH modelsCovers significant developments in the field, especially in multivariate modelsContains completely renewed chapters with new topics and resultsHandles both theoretical and applied aspectsApplies to researchers in different fields (time series, econometrics, finance)Includes numerous illustrations and applications to real financial seriesPresents a large collection of exercises with correctionsSupplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781119313571
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Hardcover. Etat : new. Hardcover. Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline 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 new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH modelsCovers significant developments in the field, especially in multivariate modelsContains completely renewed chapters with new topics and resultsHandles both theoretical and applied aspectsApplies to researchers in different fields (time series, econometrics, finance)Includes numerous illustrations and applications to real financial seriesPresents a large collection of exercises with correctionsSupplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781119313571
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Etat : New. CHRISTIAN FRANCQ, PHD, is Professor of Econometrics and Finance at CREST (Center for Research in Economics and Statistics) and ENSAE (National School of Statistics and Economic Administration).JEAN-MICHEL ZAKOIAN, PHD, is Professor of Econometrics and Finan. N° de réf. du vendeur 280990784
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Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 35113703
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Etat : New. 2019. 2nd Edition. Hardcover. . . . . . N° de réf. du vendeur V9781119313571
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