10.6084/m9.figshare.3179869
Francisco Blasques
Francisco
Blasques
André Lucas
André
Lucas
Erkki Silde
Erkki
Silde
A stochastic recurrence equations approach for score driven correlation models
Taylor & Francis Group
2016
Asymptotic normality
consistency
dynamic copulas
generalized autoregressive score models
observation driven models
stochastic recurrence equations
C22
C32
C58
2016-04-15 15:55:50
Journal contribution
https://tandf.figshare.com/articles/journal_contribution/A_stochastic_recurrence_equations_approach_for_score_driven_correlation_models/3179869
<p>We describe stationarity and ergodicity (SE) regions for a recently proposed class of score driven dynamic correlation models. These models have important applications in empirical work. The regions are derived from sufficiency conditions in Bougerol (<a href="#CIT0008" target="_blank">1993</a>) and take a nonstandard form. We show that the nonstandard shape of the sufficiency regions cannot be avoided by reparameterizing the model or by rescaling the score steps in the transition equation for the correlation parameter. This makes the result markedly different from the volatility case. Observationally equivalent decompositions of the stochastic recurrence equation yield regions with different shapes and sizes. We use these results to establish the consistency and asymptotic normality of the maximum likelihood estimator. We illustrate our results with an analysis of time-varying correlations between U.K. and Greek equity indices. We find that also in empirical applications different decompositions can give rise to different conclusions regarding the stability of the estimated model.</p>