A multivariate generalized independent factor GARCH model with an application to financial stock returns
Daniel Peña, Ester González-Prieto, Antonio García‐Ferrer · RePEc: Research Papers in Economics · 2008
We propose a new multivariate factor GARCH model, the GICA-GARCH model, where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The rst one estimates the components maximizing their non-gaussianity, and the second approach exploits the temporal structure of the data. After estimating the ICs, we t an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of nancial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one.