Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model

Annastiina Silvennoinen*, Timo Teräsvirta

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

5 Citations (Scopus)

Abstract

A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The GARCH equations of this model contain a multiplicative deterministic component to describe long-run movements in volatility and, in addition, the correlations are deterministically time-varying. Parameters of the model are estimated jointly using maximum likelihood. Consistency and asymptotic normality of maximum likelihood estimators is proved. Numerical aspects of the estimation algorithm are discussed. A bivariate empirical example is provided.

Original languageEnglish
JournalEconometrics and Statistics
Volume32
Pages (from-to)57-72
Number of pages16
ISSN2468-0389
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Deterministically varying correlation
  • Multiplicative time-varying GARCH
  • Multivariate GARCH
  • Nonstationary volatility
  • Smooth transition GARCH

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