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Sequential estimation of shape parameters in multivariate dynamic models

Dante Amengual, Gabriele Fiorentini () and Enrique Sentana ()

Journal of Econometrics, 2013, vol. 177, issue 2, 233-249

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 their efficiency, and obtain moment conditions leading to sequential estimators as efficient as their joint ML counterparts. We also obtain standard errors for VaR and CoVaR, and analyse the effects on these measures of distributional misspecification. Finally, we illustrate the small sample performance of these procedures through simulations and apply them to analyse the risk of large eurozone banks.

Keywords: Confidence intervals; Elliptical distributions; Efficient estimation; Global systematically important banks; Systemic risk; Risk management (search for similar items in EconPapers)
JEL-codes: C13 C32 G01 G11 (search for similar items in EconPapers)
Date: 2013
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Working Paper: Sequential Estimation of Shape Parameters in Multivariate Dynamic Models (2012) Downloads
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DOI: 10.1016/j.jeconom.2013.04.010

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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