Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the  exibility of univariate GARCH models coupled with parsimonious parametric models for the c...

We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation res...

The purpose of this thesis is to investigate different formulations of multivariate GARCH models and to apply two of the popular ones – the BEKKGARCH model and the DCCGARCH model – in evaluating the volatility of a portfolio of zero-coupon bonds. Multivariate GARCH models are considered as one of the most useful tools for analyzing and forecasting the volatility of time series when volatility f...

A spatiotemporal approach is proposed for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and a new bilateral Multivariate GARCH specification introduced. The covariance stationarity identification of the model studied, developing quasi-maximum-likelihood estimator analysing its consistency asymptotic normality. Further, it shown how to isol...

In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application docume...

This paper derives conditions for the existence of fourth moments of multivariate GARCH processes in the general vector specification and gives explicit results for the fourth moments and autocovariances of the squares and cross-products. Results are provided for the kurtosis and co-kurtosis between components. Applications of the results include the definition of impulse response functions for...

This paper proposes a new clustered correlation multivariate GARCH model (CCMGARCH) that allows conditional correlations to form clusters. This model can generalize the time-varying correlation structure in Tse and Tsui (2002) by determining a natural grouping of the correlations among the series. To estimate the proposed model, we adopt Markov Chain Monte Carlo methods. Two efficient sampling ...