Microstructure Noise, Realized Variance, and Optimal Sampling

Microstructure Noise, Realized Variance, and Optimal Sampling A recent and extensive literature has pioneered the summing of squared observed intra-daily returns, “realized variance”, to estimate the daily integrated variance of financial asset prices, a traditional object of economic interest. We show that, in the presence of market microstructure noise, realized variance does not identify the daily integrated variance of the frictionless equilibrium price. However, we demonstrate that the noise-induced bias at very high sampling frequencies can be appropriately traded off with the variance reduction obtained by high-frequency sampling and derive a mean-squared-error (MSE) optimal sampling theory for the purpose of integrated variance estimation. We show how our theory naturally leads to an identification procedure, which allows us to recover the moments of the unobserved noise; this procedure may be useful in other applications. Finally, using the profits obtained by option traders on the basis of alternative variance forecasts as our economic metric, we find that explicit optimization of realized variance's finite sample MSE properties results in accurate forecasts and considerable economic gains. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Review of Economic Studies Oxford University Press

Microstructure Noise, Realized Variance, and Optimal Sampling

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Publisher
Oxford University Press
Copyright
© Published by Oxford University Press.
Subject
Original Articles
ISSN
0034-6527
eISSN
1467-937X
D.O.I.
10.1111/j.1467-937X.2008.00474.x
Publisher site
See Article on Publisher Site

Abstract

A recent and extensive literature has pioneered the summing of squared observed intra-daily returns, “realized variance”, to estimate the daily integrated variance of financial asset prices, a traditional object of economic interest. We show that, in the presence of market microstructure noise, realized variance does not identify the daily integrated variance of the frictionless equilibrium price. However, we demonstrate that the noise-induced bias at very high sampling frequencies can be appropriately traded off with the variance reduction obtained by high-frequency sampling and derive a mean-squared-error (MSE) optimal sampling theory for the purpose of integrated variance estimation. We show how our theory naturally leads to an identification procedure, which allows us to recover the moments of the unobserved noise; this procedure may be useful in other applications. Finally, using the profits obtained by option traders on the basis of alternative variance forecasts as our economic metric, we find that explicit optimization of realized variance's finite sample MSE properties results in accurate forecasts and considerable economic gains.

Journal

The Review of Economic StudiesOxford University Press

Published: Apr 1, 2008

Keywords: JEL classification G12 G13

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