We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns....

We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM,...

The study concentrates on an analysis of the Czech stock market performed by an application of DCC MV GARCH model of Engle (2002). Data sample including years from 1994 to 2009 is represented by daily returns of Prague Stock Exchange index and other 11 major stock indices. There is found an...

The paper tests the CAPM for the Brazilian stock market using dynamic betas. The sample involves 28 stocks included in the Ibovespa portfolio as of March 21, 2012 and that were traded during the period from Jan. 01, 1995 to March 20, 2012. Dynamic betas were estimated and conditional betas...

This paper presents evidence of linkages across equity markets in the following transition economies: Russia, Ukraine, Poland and Czech Republic from beginning of January 2005 till the end of December 2014. We apply a multivariate asymmetric EGARCH model. Empirical results indicate significant...