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An EM Algorithm for Conditionally Heteroskedastic Factor Models

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Author Info

  • Demos, A
  • Sentana, E

Abstract

This paper discusses the application of the EM algorithm for maximum likelihood estimation of factor models with conditional heteroskedasticity in the common factors.

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Bibliographic Info

Paper provided by Centro de Estudios Monetarios Y Financieros- in its series Papers with number 9615.

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Length: 31 pages
Date of creation: 1996
Date of revision:
Handle: RePEc:fth:cemfdt:9615

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Postal: Centro de Estudios Monetarios Y Financieros. Casado del Alisal, 5-28014 Madrid, Spain.
Phone: 914290551
Fax: 914291056
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Web page: http://www.cemfi.es/
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Keywords: STATISTICS;

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Cited by:
  1. Jon Wongswan, 2003. "Contagion: an empirical test," International Finance Discussion Papers 775, Board of Governors of the Federal Reserve System (U.S.).
  2. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2003. "Likelihood-Based Estimation Of Latent Generalised Arch Structures," Working Papers. Serie AD 2003-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  3. Enrique Sentana & Giorgio Calzolari & Gabriele Fiorentini, 2004. "Indirect Estimation Of Conditionally Heteroskedastic Factor Models," Working Papers wp2004_0409, CEMFI.
  4. Francis X. Diebold & Jose A. Lopez, 1995. "Measuring Volatility Dynamics," NBER Technical Working Papers 0173, National Bureau of Economic Research, Inc.
  5. Mohamed Saidane & Christian Lavergne, 2009. "Optimal Prediction with Conditionally Heteroskedastic Factor Analysed Hidden Markov Models," Computational Economics, Society for Computational Economics, vol. 34(4), pages 323-364, November.
  6. Dunne, Peter G., 1999. "Size and book-to-market factors in a multivariate GARCH-in-mean asset pricing application," International Review of Financial Analysis, Elsevier, vol. 8(1), pages 35-52.

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