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Sequential Estimation Of Shape Parameters In Multivariate Dynamic Models

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Author Info

  • Dante Amengual

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

  • Gabriele Fiorentini

    ()
    (Università di Firenze and RCEA)

  • Enrique Sentana

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

Abstract

Sequential maximum likelihood and GMM estimators of distributional parameters obtained from the standardised innovations of multivariate conditionally heteroskedastic dynamic regression models evaluated at Gaussian PML estimators preserve the consistency of mean and variance parameters while allowing for realistic distributions. We assess the efficiency of those estimators, and obtain moment conditions leading to sequential estimators as efficient as their joint maximum likelihood counterparts. We also obtain standard errors for the quantiles required in VaR and CoVaR calculations, and analyse the effects on these measures of distributional misspecification. Finally, we illustrate the small sample performance of these procedures through Monte Carlo simulations.

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Bibliographic Info

Paper provided by CEMFI in its series Working Papers with number wp2012_1201.

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Date of creation: Feb 2012
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Handle: RePEc:cmf:wpaper:wp2012_1201

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Keywords: Elliptical distributions; Efficient estimation; Systemic risk; Value at risk.;

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References

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Cited by:
  1. Bontemps, Christian, 2013. "Moment-Based Tests for Discrete Distributions," IDEI Working Papers 772, Institut d'Économie Industrielle (IDEI), Toulouse.
  2. Gabriele Fiorentini & Enrique Sentana, 2012. "Tests For Serial Dependence In Static, Non-Gaussian Factor Models," Working Papers, CEMFI wp2012_1211, CEMFI.

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