Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Models

CEMFI Working Paper 9709

42 Pages Posted: 2 Feb 1998

See all articles by Gabriele Fiorentini

Gabriele Fiorentini

Universita di Firenze - Dipartimento di Statistica

Enrique Sentana

Centro de Estudios Monetarios y Financieros (CEMFI); Financial Markets Group; Centre for Economic Policy Research (CEPR)

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Abstract

We investigate several important inference issues for factor models with dynamic heteroskedasticity in the common factors. First, we show that such models are identified if we take into account the time-variation in the variances of the factors. Our results also apply to dynamic versions of the APT, dynamic factor models, and vector autoregressions. Secondly, we propose a consistent two-step estimation procedure which does not rely on knowledge of any factor estimates, and explain how to compute correct standard errors. Thirdly, we develop a simple preliminary LM test for the presence of ARCH effects in the common factors. Finally, we conduct a Monte Carlo analysis of the finite sample properties of the proposed estimators and hypothesis tests.

JEL Classification: C12, C51, C52

Suggested Citation

Fiorentini, Gabriele and Sentana, Enrique, Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Models. CEMFI Working Paper 9709, Available at SSRN: https://ssrn.com/abstract=56520 or http://dx.doi.org/10.2139/ssrn.56520

Gabriele Fiorentini

Universita di Firenze - Dipartimento di Statistica ( email )

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Enrique Sentana (Contact Author)

Centro de Estudios Monetarios y Financieros (CEMFI) ( email )

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