TY - JOUR
T1 - Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model
AU - Silvennoinen, Annastiina
AU - Teräsvirta, Timo
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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.
KW - Deterministically varying correlation
KW - Multiplicative time-varying GARCH
KW - Multivariate GARCH
KW - Nonstationary volatility
KW - Smooth transition GARCH
UR - http://www.scopus.com/inward/record.url?scp=85122658927&partnerID=8YFLogxK
U2 - 10.1016/j.ecosta.2021.07.008
DO - 10.1016/j.ecosta.2021.07.008
M3 - Journal article
AN - SCOPUS:85122658927
SN - 2468-0389
VL - 32
SP - 57
EP - 72
JO - Econometrics and Statistics
JF - Econometrics and Statistics
ER -