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Estimation of Stock Price Variances and Serial Covariances from Discrete Observations

Published online by Cambridge University Press:  06 April 2009

Lawrence Harris
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Abstract

Stock price discreteness adds noise to price series. The noise increases return variances and adds negative serial correlation to return series. Standard variance and serial covariance estimators therefore overestimate the variance and serial covariance of the underlying stock values. Discreteness-induced variance and serial covariance depend on underlying volatility and on the size of the bid/ask spread. Simple formulas for approximating the effects of discreteness on variance and serial correlation are derived and presented. The approximations, which are accurate in daily data, can be used to adjust the standard variance and serial covariance estimators.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 25 , Issue 3 , September 1990 , pp. 291 - 306
Copyright
Copyright © School of Business Administration, University of Washington 1990

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References

Bagehot, W. “The Only Game in Town.” Financial Analysts Journal, 27 (03/04 1971), 12–14.CrossRefGoogle Scholar
Ball, C. A.; Torous, W. A.; and Tschoegl, A. E.. “The Degree of Price Resolution: The Case of the Gold Market.” The Journal of Futures Markets, 5 (Spring 1985), 29–43.CrossRefGoogle Scholar
Copeland, T. E., and Galai, D.. “Information Effects on the Bid-Ask Spread.” Journal of Finance, 38 (12 1983), 1457–1469.Google Scholar
Easley, D., and O'Hara, M.. “Prices, Trade Size and Information in Securities Markets.” Journal of Financial Economics, 19 (09 1987), 69–90.CrossRefGoogle Scholar
French, K. R., and Roll, R.. “Stock Return Variances: The Arrival of Information and Reaction of Traders.” Journal of Financial Economics, 17 (09 1986), 5–26.CrossRefGoogle Scholar
Glosten, L. R., and Harris, L.. “Estimating the Components of the Bid/Ask Spread.” Journal of Financial Economics, 21 (05 1988), 123–142.CrossRefGoogle Scholar
Glosten, L. R., and Milgrom, P. R.. “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders.” Journal of Financial Economics, 14 (03 1985), 71–100.CrossRefGoogle Scholar
Gordin, M. I. “The Central Limit Theorem for Stationary Processes.” Soviet Mathematics–Doklady, 10 (09–10 1969), 1174–1176.Google Scholar
Gottlieb, G. and Kalay, A.. “Implications of the Discreteness of Observed Stock Prices.” Journal of Finance, 40 (03 1985), 135–153.CrossRefGoogle Scholar
Jeffreys, H.Theory of Probability (3rd ed.). Oxford: Clarendon (1966).Google Scholar
Marsh, T. A., and Rosenfeld, E. R.. “Nontrading, Market Making, and Estimates of Stock Price Volatility.” Journal of Financial Economics, 15 (03 1986), 359–372.CrossRefGoogle Scholar
Niederhoffer, V. “Clustering of Stock Prices.” Operations Research, 13 (04 1965), 258–265.CrossRefGoogle Scholar
Niederhoffer, V. “A New Look at Clustering of Stock Prices.” Operations Research, 14 (04 1966), 309–313.Google Scholar
Roll, R. “A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market.” Journal of Finance, 39 (09 1984), 1127–1139.Google Scholar
Zellner, A.An Introduction to Bayesian Inference in Econometrics. New York: John Wiley & Sons (1971).Google Scholar