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 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.--by back cover