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Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks

  • Sentana, Enrique
  • Calzolari, Giorgio
  • Fiorentini, Gabriele

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.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 146 (2008)
Issue (Month): 1 (September)
Pages: 10-25

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Handle: RePEc:eee:econom:v:146:y:2008:i:1:p:10-25
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