An EM Algorithm for Conditionally Heteroscedastic Factor Models
AbstractThis 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 InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 16 (1998)
Issue (Month): 3 (July)
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Web page: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main
Other versions of this item:
- Demos, A & Sentana, E, 1996. "An EM Algorithm for Conditionally Heteroskedastic Factor Models," Papers 9615, Centro de Estudios Monetarios Y Financieros-.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
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- Enrique Sentana & Giorgio Calzolari & Gabriele Fiorentini, 2004. "Indirect Estimation Of Conditionally Heteroskedastic Factor Models," Working Papers wp2004_0409, CEMFI.
- Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004.
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- 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.
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