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 model, 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.

Indirect estimation of large conditionally heteroskedastic factormodels, with an application to the Dow 30 stocks / E. SENTANA; G. CALZOLARI; G. FIORENTINI. - In: JOURNAL OF ECONOMETRICS. - ISSN 0304-4076. - STAMPA. - 146:(2008), pp. 10-25.

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

CALZOLARI, GIORGIO;FIORENTINI, GABRIELE
2008

Abstract

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 model, 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.
2008
146
10
25
E. SENTANA; G. CALZOLARI; G. FIORENTINI
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/316536
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