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An EM Algorithm for Conditionally Heteroscedastic Factor Models

Antonis Demos () and Enrique Sentana ()

Journal of Business & Economic Statistics, 1998, vol. 16, issue 3, 357-61

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.

Date: 1998
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Related works:
Working Paper: An EM Algorithm for Conditionally Heteroskedastic Factor Models (1996)
Working Paper: An EM Algorithm for Conditionally Heteroskedastic Factor Models (1996)
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