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.
Volume (Year): 3 (2005)
Issue (Month): 4 ()
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- 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.
- 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," Center for Financial Institutions Working Papers 01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
- S. James Press, 1967. "A Compound Events Model for Security Prices," The Journal of Business, University of Chicago Press, vol. 40, pages 317.
- 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.
- Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
- 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.
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