A Lagrange multiplier test for testing the adequacy of the constant conditional correlation GARCH model

Paul Catani, Timo Teräsvirta, Meiqun Yin

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

A Lagrange multiplier test for testing the parametric structure of a constant conditional correlation-generalized autoregressive conditional heteroskedasticity (CCC-GARCH) model is proposed. The test is based on decomposing the CCC-GARCH model multiplicatively into two components, one of which represents the null model, whereas the other one describes the misspecification. A simulation study shows that the test has good finite sample properties. We compare the test with other tests for misspecification of multivariate GARCH models. The test has high power against alternatives where the misspecification is in the GARCH parameters and is superior to other tests. The test is not greatly affected by misspecification in the conditional correlations and is therefore well suited for considering misspecification of GARCH equations.
Original languageEnglish
Peer-reviewed scientific journalEconometric Reviews
Volume36
Issue number6-9
Pages (from-to)599-621
Number of pages23
ISSN0747-4938
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article - refereed

Keywords

  • 112 Statistics and probability
  • Constant conditional correlation
  • LM test
  • misspecification testing
  • modeling volatility
  • multivariate GARCH
  • 511 Economics
  • Constant conditional correlation
  • LM test
  • misspecification testing
  • modeling volatility
  • multivariate GARCH

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