A multivariate generalized independent factor GARCH model with an application to financial stock returns
AbstractWe 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 first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit 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 financial 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.
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Bibliographic InfoPaper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws087528.
Date of creation: Dec 2008
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ICA; Multivariate GARCH; Factor models; Forecasting volatility;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-01-10 (All new papers)
- NEP-ECM-2009-01-10 (Econometrics)
- NEP-ETS-2009-01-10 (Econometric Time Series)
- NEP-FOR-2009-01-10 (Forecasting)
- NEP-RMG-2009-01-10 (Risk Management)
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