Activity Number:
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268
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Type:
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Invited
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Date/Time:
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Wednesday, August 14, 2002 : 8:30 AM to 10:20 AM
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Sponsor:
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Business & Economics Statistics Section*
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Abstract - #300254 |
Title:
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On Estimation and Prediction for Long Memory Stochastic Volatility Models
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Author(s):
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Rohit Deo*+ and Cliff Hurvich
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Affiliation(s):
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New York University and Stern School of Business, New York University
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Address:
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44 West 4th Street, New York, New York, 10012-1106,
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Keywords:
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Abstract:
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We consider GMM and QML estimation of long memory stochastic volatility models. We show that moment conditions currently used in the literature for GMM estimation have a slower rate of convergence than root n for the long memory SV model, while the QML estimator still preserves the root n rate. We provide a new set of moment conditions, which are able to preserve the root n rate for GMM estimation. We then consider issues pertaining to prediction of squared returns based not only on past squared returns but also on general powers of past absolute returns.
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