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Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series

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Author Info

  • Lucia Alessi
  • Matteo Barigozzi
  • Marco Capasso

Abstract

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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Bibliographic Info

Paper provided by Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy in its series LEM Papers Series with number 2006/25.

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Date of creation: 02 Oct 2006
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Handle: RePEc:ssa:lemwps:2006/25

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Keywords: Dynamic Factors; Multivariate GARCH; Covolatility Forecasting;

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References

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Citations

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Cited by:
  1. Lasse BORK & Hans DEWACHTER & Romain HOUSSA, 2009. "Identification of macroeconomic factors in large panels," Center for Economic Studies - Discussion papers, Katholieke Universiteit Leuven, Centrum voor Economische Studiën ces09.18, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
  2. Xisong Jin & Francisco Nadal De Simone, 2013. "Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach," BCL working papers 82, Central Bank of Luxembourg.
  3. Gao, Quansheng & Hu, Chengjun, 2009. "Dynamic mortality factor model with conditional heteroskedasticity," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 410-423, December.
  4. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models," LEM Papers Series, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy 2007/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  5. Matteo Barigozzi & Marco Capasso, 2007. "A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance," LEM Papers Series, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy 2007/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  6. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
  7. Antonio García-Ferrer & Ester González-Prieto & Daniel Peña, 2008. "A multivariate generalized independent factor GARCH model with an application to financial stock returns," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de Estadística y Econometría ws087528, Universidad Carlos III, Departamento de Estadística y Econometría.

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