Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)
Fiche du document
- handle: 10670/1.542zax
- halshs: halshs-01133751
info:eu-repo/semantics/OpenAccess
Mots-clés
Taylor expansion multivariate GARCH neural networkSujets proches
Competitive examinations TestsCiter ce document
Anne Péguin-Feissolle et al., « Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix) », HAL-SHS : économie et finance, ID : 10670/1.542zax
Métriques
Partage / Export
Résumé
We introduce two multivariate constant conditional correlation tests that require little knowledge of the functional relationship determining the conditional correlations. The first test is based on artificial neural networks and the second one is based on a Taylor expansion of each unknown conditional correlation. These new tests can be seen as general misspecification tests of a large set of multivariate GARCH-type models. We investigate the size and the power of these tests through Monte Carlo experiments. Moreover, we study their robustness to non-normality by simulating some models such as the GARCH−t and Beta−t−EGARCH models. We give some illustrative empirical examples based on financial data.