Generalized dynamic factor model + GARCH exploiting multivariate information for univariate prediction

Arbeitspapier

We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large 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 two samples up to 171 series, providing one-step-ahead volatility predictions of returns. The GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust with respect to different volatility proxies.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: LEM Working Paper Series ; No. 2006/13

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods
• Thema: Dynamic Factors ; GARCH ; volatility forecasting ; Prognoseverfahren ; Multivariate Analyse ; ARCH-Modell ; Kapitaleinkommen ; Aktienmarkt ; USA

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Alessi, Lucia ; Barigozzi, Matteo ; Capasso, Marco
• Ereignis: Veröffentlichung
• (wer): Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
• (wo): Pisa
• (wann): 2007

Weitere Informationen

• Handle: http://hdl.handle.net/10419/89540
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ