Gabriele Fiorentini () (University of Florence and The Rimini Centre for Economics Analysis, Italy.) Giorgio Calzolari () (University of Florence) Enrique Sentana () (CEMFI, Spain)
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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 Rimini Centre for Economic Analysis in its series Working Paper Series with number
40-07.