Sequential Estimation Of Shape Parameters In Multivariate Dynamic Models
AbstractSequential 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 InfoPaper provided by CEMFI in its series Working Papers with number wp2012_1201.
Date of creation: Feb 2012
Date of revision:
Elliptical distributions; Efficient estimation; Systemic risk; Value at risk.;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-07-01 (All new papers)
- NEP-ECM-2012-07-01 (Econometrics)
- NEP-ETS-2012-07-01 (Econometric Time Series)
- NEP-ORE-2012-07-01 (Operations Research)
- NEP-RMG-2012-07-01 (Risk Management)
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