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

  • Gabriele Fiorentini


    (University of Florence and The Rimini Centre for Economics Analysis, Italy.)

  • Giorgio Calzolari


    (University of Florence)

  • Enrique Sentana


    (CEMFI, Spain)

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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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 40-07.

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Date of creation: Jul 2007
Date of revision: Jul 2007
Handle: RePEc:rim:rimwps:40-07
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