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Tests For Serial Dependence In Static, Non-Gaussian Factor Models

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  • Gabriele Fiorentini

    ()
    (Università di Firenze)

  • Enrique Sentana

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

Abstract

We derive simple algebraic expressions for score tests of serial correlation in the levels and squares of common and idiosyncratic factors in static factor models with (semi) parametrically specified elliptical distributions even though one must generally compute the likelihood by simulation. We also robustify our Gaussian tests against non-normality. The orthogonality conditions resemble the orthogonality conditions of models with observed factors but the weighting matrices reflect their unobservability. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests, and compare them to other existing procedures. Finally, we apply our methods to monthly US stock returns.

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Bibliographic Info

Paper provided by CEMFI in its series Working Papers with number wp2012_1211.

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Date of creation: Oct 2012
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Handle: RePEc:cmf:wpaper:wp2012_1211

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Related research

Keywords: ARCH; Financial returns; Kalman filter; LM tests; Non-Gaussian state space models; Predictability.;

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  1. Tauchen, George, 1985. "Diagnostic testing and evaluation of maximum likelihood models," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 415-443.
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  5. Mencía, Javier & Sentana, Enrique, 2009. "Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation," Journal of Econometrics, Elsevier, vol. 153(2), pages 105-121, December.
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  9. Gabriele Fiorentini & Giorgio Calzolari & Enrique Sentana, 2007. "Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks," Working Paper Series 40-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
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