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Abstract Details
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Activity Number:
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308
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #300841 |
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Title:
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GEL Estimation for Semi-Strong Nonlinear GARCH with Robust Empirical Likelihood Inference
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Author(s):
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Jonathan Hill*+ and Artem Prokhorov
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Companies:
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The University of North Carolina and Concordia University
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Address:
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Dept. of Economics, Chapel Hill, NC, 27599, USA
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Keywords:
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Empirical Likelihood ;
Nonlinear GARCH ;
Tail Trimming ;
Empirical Likelihood Ratio Test
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Abstract:
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We construct Generalized Empirical Likelihood estimators for random volatility models of heavy-tailed data with particular attention to nonlinear GARCH. The estimator imbeds trimmed estimating equations allowing for over-identifying conditions, consistency, asymptotic normality and efficiency for very heavy-tailed data due to feedback or idiosyncratic noise. As opposed to existing heavy tailed robust QML and LAD estimators for random volatility (Ling 2005, Peng and Yao 2003, Linton et al 2010) we allow for model asymmetries and over identifying restrictions. We use the theory of GEL with tail-trimming to construct several robust tests that uses as plug-in any consistent estimator of the parameter and likewise characterize a class of efficient moment estimators.
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