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Activity Number:
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398
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Type:
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Invited
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Date/Time:
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Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #308180 |
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Title:
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Conditional Quantile Estimation for GARCH Models
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Author(s):
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Zhijie Xiao*+
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Companies:
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Boston College
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Address:
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Dept of Economics, Chestnut Hill, MA, 02467,
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Keywords:
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Quantile Regression ; GARCH ; Nonlinear
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Abstract:
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The purpose of this paper is to propose a robust, flexible approach to estimating conditional quantiles in time series with GARCH structure. Conditional quantiles is an essential ingredient in various risk measures, and the GARCH process has proven to be highly successful in modeling financial return data. However, quantile regression estimation of GARCH models is highly nonlinear, and can not be directly estimated by traditional recursive methods. We propose two new methods of estimating quantiles of GARCH models. The first method is based on minimum-distance estimation from a first stage nonlinear quantile regression. The second method is based on a preliminary sieve quantile regression. Asymptotic properties of both methods are investigated.
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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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