This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

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

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Lucia Alessi
Matteo Barigozzi
Marco Capasso

Additional information is available for the following registered author(s):

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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.lem.sssup.it/WPLem/files/2006-25.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher 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.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 02 Oct 2006
Date of revision:
Handle: RePEc:ssa:lemwps:2006/25

Contact details of provider:
Postal: Piazza dei Martiri della Liberta, 33, 56127 Pisa
Phone: +39-50-883343
Fax: +39-50-883344
Email:
Web page: http://www.lem.sssup.it/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: ().

Related research
Keywords: Dynamic Factors; Multivariate GARCH; Covolatility Forecasting;

Other versions of this item:

This paper has been announced in the following NEP Reports: References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," Working Papers 284, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
    Other versions:
  2. Engle, Robert F. & Ng, Victor K. & Rothschild, Michael, 1990. "Asset pricing with a factor-arch covariance structure : Empirical estimates for treasury bills," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 213-237. [Downloadable!] (restricted)
    Other versions:
  3. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-27, July. [Downloadable!] (restricted)
    Other versions:
  4. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Generalized Dynamic Factor Model + GARCH
    Exploiting Multivariate Information for Univariate Prediction    ," LEM Papers Series 2006/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  5. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 674, European Central Bank. [Downloadable!]
    Other versions:
  6. Sentana, E. & Fiorentini, G., 1997. "Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Model," Papers 9709, Centro de Estudios Monetarios Y Financieros-.
    Other versions:
  7. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446. [Downloadable!]
  8. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2007. "A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering," CEPR Discussion Papers 6043, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  9. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
    Other versions:
  10. Harvey, Andrew & Ruiz, Esther & Sentana, Enrique, 1992. "Unobserved component time series models with Arch disturbances," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 129-157. [Downloadable!] (restricted)
  11. King, Mervyn & Sentana, Enrique & Wadhwani, Sushil, 1994. "Volatility and Links between National Stock Markets," Econometrica, Econometric Society, vol. 62(4), pages 901-33, July. [Downloadable!] (restricted)
    Other versions:
  12. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-304, September. [Downloadable!] (restricted)
    Other versions:
  13. Robert F. Engle, 2000. "Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models," University of California at San Diego, Economics Working Paper Series 2000-09, Department of Economics, UC San Diego. [Downloadable!]
  14. David H. Small & Domenico Giannone & Lucrezia Reichlin, 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 633, European Central Bank. [Downloadable!]
    Other versions:
  15. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis. [Downloadable!]
  16. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November. [Downloadable!] (restricted)
    Other versions:
  17. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March. [Downloadable!] (restricted)
    Other versions:
  18. Diebold, Francis X & Nerlove, Marc, 1989. "The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor Arch Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(1), pages 1-21, Jan.-Mar.. [Downloadable!] (restricted)
    Other versions:
  19. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  20. Connor, Gregory & Korajczyk, Robert A. & Linton, Oliver, 2006. "The common and specific components of dynamic volatility," Journal of Econometrics, Elsevier, vol. 132(1), pages 231-255, May. [Downloadable!] (restricted)
  21. Nijman, Theo & Sentana, Enrique, 1996. "Marginalization and contemporaneous aggregation in multivariate GARCH processes," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 71-87. [Downloadable!] (restricted)
    Other versions:
  22. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. [Downloadable!] (restricted)
  23. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Blackwell Publishing, vol. 61(2), pages 247-64, April. [Downloadable!] (restricted)
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Matteo Barigozzi & Marco Capasso, 2007. "A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance," LEM Papers Series 2007/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  2. 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 ws087528, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  3. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2008. "A robust criterion for determining the number of static factors in approximate factor models," Working Paper Series 903, European Central Bank. [Downloadable!]
    Other versions:
Statistics
Access and download statistics

Did you know? Over 80% of the top 1000 economists are registered on RePEc.

This page was last updated on 2009-11-13.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.