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Activity Number:
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17
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 3, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301733 |
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Title:
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Rank-Based Estimation for GARCH Models
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Author(s):
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Beth Andrews*+
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Companies:
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Northwestern University
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Address:
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2006 Sheridan Road, Evanston, IL, 60208,
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Keywords:
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GARCH ; time series ; rank estimation
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Abstract:
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A rank-based technique is used to estimate the parameters of generalized autoregressive conditionally heteroscedastic (GARCH) time series models. The estimators minimize the sum of mean-corrected model residuals weighted by a function of residual rank. Rank estimators are, in general, robust and relatively efficient. We show this is true in the case of GARCH parameter estimation. The estimation technique is robust because the rank estimators of GARCH model parameters are consistent and asymptotically normal under mild conditions. Since the weight function can be chosen so that rank estimation has the same asymptotic efficiency as maximum likelihood estimation, the rank estimators are also relatively efficient. In addition, rank estimation dominates classical Gaussian quasi-maximum likelihood estimation with respect to both robustness and asymptotic efficiency.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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