2006/13 LEM Working Paper Series

Generalized Dynamic Factor Model + GARCH
Exploiting Multivariate Information for Univariate Prediction

Lucia Alessi, Matteo Barigozzi, Marco Capasso
Dynamic Factors, GARCH, Volatility Forecasting.

  JEL Classifications
C32, C52, C53.

We 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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