Realized Variance and Market Microstructure Noise
نویسندگان
چکیده
We study market microstructure noise in high-frequency data and analyze its implications for the realized variance (RV) under a general specification for the noise. We show that kernel-based estimators can unearth important characteristics of market microstructure noise and that a simple kernel-based estimator dominates the RV for the estimation of integrated variance (IV). An empirical analysis of the Dow Jones Industrial Average stocks reveals that market microstructure noise is time-dependent and correlated with increments in the efficient price. This has important implications for volatility estimation based on highfrequency data. Finally, we apply cointegration techniques to decompose transaction prices and bid–ask quotes into an estimate of the efficient price and noise. This framework enables us to study the dynamic effects on transaction prices and quotes caused by changes in the efficient price.
منابع مشابه
Microstructure Noise , Realized Variance , and Optimal Sampling ∗
Observed asset prices are known to deviate from their efficient values due to market microstructure frictions. This paper studies the effects of market microstructure noise on nonparametric estimates of the efficient price integrated variance. Specifically, we consider both asymptotic and finite sample effects of general market microstructure noise on realized variance estimates. The finite sam...
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If efficient asset prices follow a semi-martingale and are perfectly observed, their quadratic variation can be measured accurately from the sum of a large number of squared returns sampled over very finely spaced intervals, i.e., realized variance (Andersen et al., 2003, and Barndorff-Nielsen and Shephard, 2002). With the emergence of high-frequency data, it seems that we should be able to ide...
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If efficient asset prices follow continuous semi-martingales and are perfectly observed, their quadratic variation can be measured accurately from the sum of a large number of squared returns sampled over very finely spaced intervals, i.e., realized variance (Andersen et al., 2003, and Barndorff-Nielsen and Shephard, 2002). With the emergence of high-frequency data, it seems that we should be a...
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