Estimation of multivariate GARCH models is usually carried out by quasi maximum likelihood (QMLE), for which recently consistency and asymptotic normality have been proven under quite general conditions. However, there are to date no results on the efficiency loss of QMLE if the true innovation distribution is not multinormal. We investigate this issue by suggesting a nonparametric estimation o...

Volatility modelling of asset returns is an important aspect for many financial applications, e.g., option pricing and risk management. GARCH models are usually used to model the volatility processes of financial time series. However, multivariate GARCH modelling of volatilities is still a challenge due to the complexity of parameters estimation. To solve this problem, we suggest using Independ...

Correlations among the asset returns are the main reason for the computational and statistical complexities of the full multivariate GARCH models. We rely on the variancecorrelation separation strategy and introduce a broad class of multivariate models in the spirit of Engle’s (2002) dynamic conditional correlation models, that is univariate GARCH models are used for variances of individual ass...

Correlations among the asset returns are the main reason for the computational and statistical complexities of the full multivariate GARCH models. We rely on the variancecorrelation separation strategy and introduce a broad class of multivariate models in the spirit of Engle’s (2002) dynamic conditional correlation models, that is univariate GARCH models are used for variances of individual ass...

Instantaneous dependence among several asset returns is the main reason for the computational and statistical complexities in working with full multivariate GARCH models. Using the Cholesky decomposition of the covariance matrix of such returns, we introduce a broad class of multivariate models where univariate GARCH models are used for variances of individual assets and parsimonious models for...