Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153549
Authors: 
Alessi, Lucia
Barigozzi, Matteo
Capasso, Marco
Year of Publication: 
2009
Series/Report no.: 
ECB Working Paper 1115
Abstract: 
We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The information contained in large datasets is captured by few dynamic common factors, which we assume being conditionally heteroskedastic. After presenting the model, we propose a multi-step estimation technique which combines asymptotic principal components and multivariate GARCH. We also prove consistency of the estimated conditional covariances. We present simulation results in order to assess the finite sample properties of the estimation technique. Finally, we carry out two empirical applications respectively on macroeconomic series, with a particular focus on different measures of inflation, and on financial asset returns. Our model outperforms the benchmarks in fore-casting the inflation level, its conditional variance and the volatility of returns. Moreover, we are able to predict all the conditional covariances among the observable series.
Subjects: 
Conditional Covariance
Dynamic Factor Models
Inflation forecasting
multivariate GARCH
Volatility Forecasting
JEL: 
C52
C53
Document Type: 
Working Paper

Files in This Item:
File
Size
958.38 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.