English, Article, Journal or magazine article edition: Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks Gabriele Fiorentini; Giorgio Calzolari; Enrique Sentana

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Bookmark: http://trove.nla.gov.au/version/145313
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks
Author
  • Gabriele Fiorentini
  • Giorgio Calzolari
  • Enrique Sentana
Physical Description
  • preprint
Notes
  • We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for large-scale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small sample behaviour of our indirect estimators and several likelihood-based procedures through an extensive Monte Carlo experiment with empirically realistic designs. Finally, we apply our procedures to weekly returns on the Dow 30 stocks.
  • ARCH, Idiosyncratic risk, Inequality constraints, Kalman filter, Sequential estimators, Simulation estimators, Volatility.
  • RePEc:rim:rimwps:40-07
Language
  • English
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