Forecasting Large Datasets with Conditionally Heteroskedastic Dynamic Common Factors
AbstractWe propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation results and carry out two empirical applications on financial asset returns and macroeconomic series, with a particular focus on different measures of inflation. Our proposed model outperforms the benchmarks in forecasting the conditional volatility of returns and the inflation level. Moreover, it allows to predict conditional covariances of all the time series in the panel.
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Bibliographic InfoPaper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number 2009_005.
Length: 40 p.
Date of creation: 2009
Date of revision:
Publication status: Published by:
Dynamic factors; multivariate GARCH; covolatility forecasting; inflation forecasting;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-03-14 (All new papers)
- NEP-ECM-2009-03-14 (Econometrics)
- NEP-ETS-2009-03-14 (Econometric Time Series)
- NEP-FOR-2009-03-14 (Forecasting)
- NEP-ORE-2009-03-14 (Operations Research)
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