This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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476
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Risk Analysis
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Abstract - #308066 |
Title:
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Regularization for Stationary Multivariate Time Series
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Author(s):
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Yan Sun*+ and Xiaodong Lin
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Companies:
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University of Cincinnati and Rutgers University
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Address:
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2600 Clifton Ave., Cincinnati, OH, 45221,
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Keywords:
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Multivariate ;
GARCH ;
Penalty ;
Sparsity ;
Oracle Property
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Abstract:
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The complexity of multivariate time series models grows dramatically when the number of component series increases. In this paper, we develop a regularization framework for multivariate time series models based on the penalized likelihood method. We show that under mild conditions, the regularized estimators are sparse-consistent and possess the well-known oracle property. This framework provides a theoretical foundation for addressing the curse of dimensionality in multivariate econometric models. We illustrate the utility of our method by developing a sparse version of the full-factor multivariate GARCH model. We successfully apply this model to simulated data as well as the daily log return data of the Dow Jones industrial average component stocks.
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