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Abstract Details
Activity Number:
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623
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #300613 |
Title:
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Efficient Semiparametric GARCH Modeling of Financial Volatility
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Author(s):
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Li Wang and Qiongxia Song*+ and Lijian Yang
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Companies:
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University of Georgia and The University of Texas at Dallas and Michigan State University
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Address:
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Department of Mathematical Sciences, FO35., Richardson, TX, 75080,
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Keywords:
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B-spline ;
confidence band ;
knots ;
news impact curve ;
volatility
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Abstract:
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We consider a class of semiparametric GARCH models with additive autoregressive components linked together by a dynamic coefficient. We propose estimators for the additive components and the dynamic coefficient based on spline smoothing. The estimation procedure involves only a small number of least squares operations, thus it is computationally efficient. Under regularity conditions, the proposed estimator of the parameter is root-$n$ consistent and asymptotically normal. A simultaneous confidence band for the nonparametric component is proposed by an efficient one-step spline backfitting. The performance of our method is evaluated by various simulated processes and a financial return series. For the empirical financial return series, we find further statistical evidence of the asymmetric news impact function.
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