Indirect estimation of large conditionally heteroskedastic factor models,with an application to the Dow 30 stocks |
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Authors: | Enrique Sentana Giorgio Calzolari Gabriele Fiorentini |
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Institution: | 1. CEMFI, Casado del Alisal 5, E-28014 Madrid, Spain;2. Università di Firenze, Viale Morgagni 59, I-50134 Firenze, Italy |
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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 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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Keywords: | C13 C15 C32 |
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