We propose a simultaneous equation system with GARCH errors to model the contemporaneous relations among Asian and American stock markets. On the estimated residuals, we evaluate the correlation matrix over rolling windows and introduce a correlation matrix distance, which allows both a...

In this paper we provide evidence that the effects of the different waves of asset purchase programmes implemented by the ECB from 2009 onwards have spilled over into asset price volatility developments of a group of six Central and Eastern European economies belonging to the EU but not to the...

Expected returns and risk assessment are important issues when evaluating capital investment projects. We use VARX-MGARCH models and asset pricing theory to model the expected rate of return in Brazil, Colombia, Mexico and Peru for late 2006. The main objective of this paper is to present an...

We present a multivariate generalization of the mixed normal GARCH model proposed in Haas, Mittnik, and Paolella (2004a). Issues of parametrization and estimation are discussed. We derive conditions for covariance stationarity and the existence of the fourth moment, and provide expressions for...

An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic...

We develop a multivariate generalization of the Markov-switching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourth-moment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a...

We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns....

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...

Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional...

We implement a long-horizon static and dynamic portfolio allocation involvinga risk-free and a risky asset. This model is calibrated at a quarterly frequencyfor ten European countries. We also use maximum-likelihood estimates andBayesian estimates to account for parameter uncertainty. We nd that...