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

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  • Lucia Alessi
  • Matteo Barigozzi
  • Marco Capasso

Abstract

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.

Suggested Citation

  • Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Generalized Dynamic Factor Model + GARCH Exploiting Multivariate Information for Univariate Prediction," LEM Papers Series 2006/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    • Handle: RePEc:ssa:lemwps:2006/13
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    Cited by:

    1. Jin, Xisong & Nadal De Simone, Francisco de A., 2014. "Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 81-101.
    2. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series," LEM Papers Series 2006/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.

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    Keywords

    Dynamic Factors; GARCH; Volatility Forecasting;
    All these keywords.

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