GARCH Models: Structure, Statistical Inference and Financial Applications - Hardcover

Über die Autorin bzw. den Autor

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.

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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, examines financial applications, and explores the very validation of the models used.

GARCH Models: Structure, Statistical Inference and Financial Applications, Second Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter entitled 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, amongst 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.

This book also:

• Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models
• Covers significant developments in the field, especially in multivariate models
• Contains completely renewed chapters with new topics and results
• Handles both theoretical and applied aspects
• Applies to researchers in different fields (time series, econometrics, finance)
• Includes numerous illustrations and applications to real financial series
• Presents a large collection of exercises with corrections
• Supplemented by a supporting website featuring R codes, Fortran programs, data sets and problems with corrections

GARCH Models: Structure, Statistical Inference and Financial Applications, Second 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.

Aus dem Klappentext

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, examines financial applications, and explores the very validation of the models used.

GARCH Models: Structure, Statistical Inference and Financial Applications, Second Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter entitled 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, amongst 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.

This book also:

• Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models
• Covers significant developments in the field, especially in multivariate models
• Contains completely renewed chapters with new topics and results
• Handles both theoretical and applied aspects
• Applies to researchers in different fields (time series, econometrics, finance)
• Includes numerous illustrations and applications to real financial series
• Presents a large collection of exercises with corrections
• Supplemented by a supporting website featuring R codes, Fortran programs, data sets and problems with corrections

GARCH Models: Structure, Statistical Inference and Financial Applications, Second 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.