Garch Models: Structure, Statistical Inference and Financial Applications

✍ Scribed by Christian Francq, Jean-Michel Zakoian

• Publisher: Wiley-Blackwell
• Year: 2019
• Tongue: English
• Leaves: 492
• Edition: 2
• Category: Library

✦ Synopsis

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 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, 2^nd 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.

✦ Table of Contents

Cover
GARCH Models:

Structure, Statistical Inference
and Financial Applications
© 2019
Contents
Preface to the Second Edition
Preface to the First Edition
Notation
1 Classical Time Series Models
and Financial Series
Part I:

Univariate GARCH Models
2
GARCH(p, q) Processes
3 Mixing
4 Alternative Models
for the Conditional Variance
Part II:

Statistical Inference
5
Identification
6 Estimating ARCH Models
by Least Squares
7 Estimating GARCH Models
by Quasi-Maximum Likelihood
8 Tests Based on the Likelihood
9 Optimal Inference and
Alternatives to the QMLE
Part III:

Extensions and Applications
10
Multivariate GARCH Processes
11 Financial Applications
12 Parameter-Driven
Volatility Models
Appendix A.

Ergodicity, Martingales, Mixing
Appendix B.

Autocorrelation and Partial
Autocorrelation
Appendix C.

Markov Chains on Countable
State Spaces
Appendix D.

The Kalman Filter
Appendix E.

Solutions to the Exercises
References
Index