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An EM Algorithm for Conditionally Heteroscedastic Factor Models

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  • Demos, Antonis
  • Sentana, Enrique

Abstract

This article discusses the application of the EM algorithm to factor models with dynamic heteroscedasticity in the common factors. It demonstrates that the EM algorithm reduces the computational burden so much that researchers can estimate such models with many series. Two empirical applications with 11 and 266 stock returns are presented, confirming that the EM algorithm yields significant speed gains and that it makes unnecessary the computation of good initial values. Near the optimum, however, it slows down significantly. Then, the best practical strategy is to switch to a first-derivative-based method.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 16 (1998)
Issue (Month): 3 (July)
Pages: 357-61

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Handle: RePEc:bes:jnlbes:v:16:y:1998:i:3:p:357-61

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Cited by:
  1. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2002. "Likelihood-based estimation of latent generalised ARCH structures," Economics Papers 2002-W19, Economics Group, Nuffield College, University of Oxford.
  2. Enrique Sentana & Giorgio Calzolari & Gabriele Fiorentini, 2004. "Indirect Estimation Of Conditionally Heteroskedastic Factor Models," Working Papers wp2004_0409, CEMFI.
  3. 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.
  4. 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.
  5. Francis X. Diebold & Jose A. Lopez, 1995. "Measuring Volatility Dynamics," NBER Technical Working Papers 0173, National Bureau of Economic Research, Inc.
  6. Jon Wongswan, 2003. "Contagion: an empirical test," International Finance Discussion Papers 775, Board of Governors of the Federal Reserve System (U.S.).

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