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Sequential estimation of shape parameters in multivariate dynamic models
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DOI：10.1016/j.jeconom.2013.04.010.png)
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Abstract
En 中文
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. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Confidence intervals
Elliptical distributions
Efficient estimation
Global systematically important banks
Systemic risk
Risk management
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