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Abstract Details
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Activity Number:
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133
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #306852 |
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Title:
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Regularized Realized Covariance Estimator Under Market Microstructure Noise
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Author(s):
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Changgee Chang*+
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Companies:
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The University of Chicago
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Address:
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5734 S. University Ave, Chicago, IL, 60637, United States
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Keywords:
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Abstract:
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We propose a new realized covariance matrix estimator for multivariate high frequency data that deals with the market microstructure noise. The realized kernel method is cleverly extended to accomodate the multivariate asynchronously observed data. Our estimator does not artificially sychoronize the data and uses all data to achieve efficiency. The extension is so natural that the estimator does not rely on changing the time scale and allows random sampling intervals. We show that our estimator achieves the optimal rate of convergence(n^{1/4}) under iid noise assumption. We also discuss how the estimator can be regularized to ensure positive-definiteness.
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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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