The performance of bid-ask spread estimators under less than ideal conditions
Michael Bleaney and
Zhiyong Li ()
Discussion Papers from University of Nottingham, School of Economics
The performance of bid-ask spread estimators is investigated using simulation experiments. All estimators are much more accurate if the data are sampled at high frequency. In high-frequency data, the Huang-Stoll estimator, which requires order flow information, generally outperforms Roll-type estimators based on price information only. The exception is when there is feedback trading (order flows respond to past price movements), when the Huang-Stoll estimator is seriously biased. When only low-frequency (e.g. daily) data are available, the Corwin-Schultz estimator based on daily high and low prices is usually less inaccurate than the Huang-Stoll and Roll estimators. An important and empirically relevant exception is when the spread varies within the day; in this case the Corwin-Schultz estimator significantly overestimates the true spread. For a published version, please see Studies in Economics and Finance, Vol. 32 (2015).
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Journal Article: The performance of bid-ask spread estimators under less than ideal conditions (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:not:notecp:13/05
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