An EM Algorithm for Conditionally Heteroscedastic Factor Models

Autor:	Antonis Demos, Enrique Sentana
Rok vydání:	1998
Předmět:	Statistics and Probability Economics and Econometrics Heteroscedasticity Computation Expectation–maximization algorithm Economics Econometrics Statistics Probability and Uncertainty Social Sciences (miscellaneous) Stock (geology) Factor analysis
Zdroj:	Journal of Business & Economic Statistics. 16:357-361
ISSN:	1537-2707 0735-0015
DOI:	10.1080/07350015.1998.10524775
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1457305e10787245b4ee19a6752015eb https://doi.org/10.1080/07350015.1998.10524775 Zobrazit plný text záznamu