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Abstract Details
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Activity Number:
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551
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #300152 |
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Title:
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Between Data Cleaning and Inference: Pre-Averaging and Robust Estimators of the Efficient Price
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Author(s):
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Per Mykland*+ and Lan Zhang
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Companies:
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The University of Chicago and University of Illinois at Chicago
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Address:
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, Chicago, IL, 60637, USA
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Keywords:
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finance ;
high frequency data ;
econometrics ;
volatility ;
contiguity ;
large data sets
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Abstract:
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Pre-averaging is increasingly used as a method to reduce noise in financial data. So far, theory has been derived on an estimator-by-estimator basis. This paper provides a general theory for this methodology. We find a process characterization for the estimated efficient price, and inference can be done directly on the new process. This permits the study of substantially harder inference situations. For baseline volatility inference, it also shows that pre-averaging yields estimators that are arbitrarily close to being statistically efficient.
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