Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes

Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes In this article I study the statistical properties of a bias-corrected realized variance measure when high-frequency asset prices are contaminated with market microstructure noise. The analysis is based on a pure jump process for asset prices and explicitly distinguishes among different sampling schemes, including calendar time, business time, and transaction time sampling. Two main findings emerge from the theoretical and empirical analysis. First, based on the mean-squared error (MSE) criterion, a bias correction to realized variance (RV) allows for the more efficient use of higher frequency data than the conventional RV estimator. Second, sampling in business time or transaction time is generally superior to the common practice of calendar time sampling in that it leads to a further reduction in MSE. Using IBM transaction data, I estimate a 2.5-minute optimal sampling frequency for RV in calendar time, which drops to about 12 seconds when a first-order bias correction is applied. This results in a more than 65% reduction in MSE. If, in addition, prices are sampled in transaction time, a further reduction of about 20% can be achieved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Financial Econometrics Oxford University Press

Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes

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Publisher
Oxford University Press
Copyright
© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.
ISSN
1479-8409
eISSN
1479-8417
D.O.I.
10.1093/jjfinec/nbi027
Publisher site
See Article on Publisher Site

Abstract

In this article I study the statistical properties of a bias-corrected realized variance measure when high-frequency asset prices are contaminated with market microstructure noise. The analysis is based on a pure jump process for asset prices and explicitly distinguishes among different sampling schemes, including calendar time, business time, and transaction time sampling. Two main findings emerge from the theoretical and empirical analysis. First, based on the mean-squared error (MSE) criterion, a bias correction to realized variance (RV) allows for the more efficient use of higher frequency data than the conventional RV estimator. Second, sampling in business time or transaction time is generally superior to the common practice of calendar time sampling in that it leads to a further reduction in MSE. Using IBM transaction data, I estimate a 2.5-minute optimal sampling frequency for RV in calendar time, which drops to about 12 seconds when a first-order bias correction is applied. This results in a more than 65% reduction in MSE. If, in addition, prices are sampled in transaction time, a further reduction of about 20% can be achieved.

Journal

Journal of Financial EconometricsOxford University Press

Published: Aug 26, 2005

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