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. Copyright 2005, Oxford University Press.
(This abstract was borrowed from another version of this item.)
|Date of creation:||2004|
|Contact details of provider:|| Postal: Coventry, CV4 7AL|
Phone: +44 (0)24 76524118
Fax: +44 (0)24 76524167
Web page: http://web.warwick.ac.uk/fac/soc/financeRepec/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bernard Bollen & Brett Inder, 1999.
"Estimating Daily Volatility in Financial Markets Utilizing Intraday Data,"
1999.01, School of Economics, La Trobe University.
- Bollen, Bernard & Inder, Brett, 2002. "Estimating daily volatility in financial markets utilizing intraday data," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 551-562, December.
- Bernard Bollen & Brett Inder, 1999. "Estimating Daily Volatility in Financial Markets Utilizing Intraday Data," Working Papers 1999.01, School of Economics, La Trobe University.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001.
"Modeling and Forecasting Realized Volatility,"
Center for Financial Institutions Working Papers
01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
- Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
- Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
- S. James Press, 1967. "A Compound Events Model for Security Prices," The Journal of Business, University of Chicago Press, vol. 40, pages 1-317.
When requesting a correction, please mention this item's handle: RePEc:wbs:wpaper:wp04-15. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Rong Leng)
If references are entirely missing, you can add them using this form.