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

Gabriele Fiorentini (), Giorgio Calzolari and Enrique Sentana ()

Working Paper series from Rimini Centre for Economic Analysis

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.

Keywords: ARCH; Idiosyncratic risk; Inequality constraints; Kalman filter; Sequential estimators; Simulation estimators; Volatility (search for similar items in EconPapers)
JEL-codes: C13 C15 C32 (search for similar items in EconPapers)
Date: 2007-07
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Journal Article: Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks (2008) Downloads
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