Arbeitspapier
Dynamic factor GARCH: Multivariate volatility forecast for a large number of series
We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series; it also provides a first identification and estimation of the dynamic factors governing the data set. A time-varying correlation GARCH model applied on the estimated dynamic factors finds the parameters governing their covariances' evolution. A method is suggested for estimating and predicting conditional variances and covariances of the original data series. We suggest also a modified version of the Kalman filter as a way to get a more precise estimation of the static and dynamic factors' in-sample levels and covariances in order to achieve better forecasts. Simulation results on different panels with large time and cross sections are presented. Finally, we carry out an empirical application aiming at comparing estimates and predictions of the volatility of financial asset returns. The Dynamic Factor GARCH model outperforms the univariate GARCH.
- Sprache
-
Englisch
- Erschienen in
-
Series: LEM Working Paper Series ; No. 2006/25
- 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
Multivariate GARCH
Covolatility Forecasting
Prognoseverfahren
Multivariate Analyse
ARCH-Modell
Volatilität
Aktienmarkt
Großbritannien
- 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
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Alessi, Lucia
- Barigozzi, Matteo
- Capasso, Marco
- Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
Entstanden
- 2007