2006/25 | LEM Working Paper Series | |
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Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series | ||
Lucia Alessi, Matteo Barigozzi, Marco Capasso |
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Keywords | ||
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Dynamic Factors, Multivariate GARCH, Covolatility Forecasting.
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JEL Classifications | ||
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C32, C52, C53.
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Abstract | ||
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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.
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Downloads | ||
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