Multivariate GARCH models for large-scale applications: A survey

Kris Boudt*, Alexios Galanos, Scott Payseur, Eric Zivot

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

This chapter provides a survey of various multivariate GARCH specifications that model the temporal dependence in the second moment of multivariate return series processes. The survey is focused on feasible multivariate GARCH models for large-scale applications, as well as on recent contributions in outlier-robust MGARCH analysis and the use of high-frequency returns or the score for covariance modeling. We discuss their likelihood-based estimation and application to forecasting and simulation with software implementations in the R-programming language.

Original languageEnglish
Title of host publicationHandbook of Statistics
EditorsHrishikesh D. Vinod, C.R. Rao
PublisherElsevier Science B.V.
Chapter7
Pages193-242
Number of pages50
ISBN (Electronic)9780444641533
ISBN (Print)9780444643117
DOIs
Publication statusPublished - Feb 2019

Publication series

NameHandbook of Statistics
Volume41
ISSN (Print)0169-7161

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Keywords

  • Comovement
  • Distribution
  • Time series
  • Volatility

Cite this

Boudt, K., Galanos, A., Payseur, S., & Zivot, E. (2019). Multivariate GARCH models for large-scale applications: A survey. In H. D. Vinod, & C. R. Rao (Eds.), Handbook of Statistics (pp. 193-242). (Handbook of Statistics; Vol. 41). Elsevier Science B.V.. https://doi.org/10.1016/bs.host.2019.01.001