Generalized Dynamic Factor Model + GARCH Exploiting Multivariate Information for Univariate Prediction
AbstractWe propose a new model for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM)and the GARCH model. The GDFM, applied to a huge number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and standard GARCH performance on samples up to 475 series, predicting both levels and volatility of returns. While results on levels are not significantly different, on volatility the GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust with respect to different volatility proxies.
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Bibliographic InfoPaper provided by Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy in its series LEM Papers Series with number 2006/13.
Date of creation: 13 May 2006
Date of revision:
Dynamic Factors; GARCH; Volatility Forecasting;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-05-20 (All new papers)
- NEP-ECM-2006-05-20 (Econometrics)
- NEP-ETS-2006-05-20 (Econometric Time Series)
- NEP-FIN-2006-05-20 (Finance)
- NEP-FMK-2006-05-20 (Financial Markets)
- NEP-FOR-2006-05-20 (Forecasting)
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