An Optimal and Unbiased Measure of Realized Variance Based on Intermittent High-Frequency Data
نویسندگان
چکیده
We consider the problem of estimating a measure of daily volatility from intermittent high-frequency data that are subject to market microstructure effects. We show that a simple Newey-West type modification of the realized variance (RV) yields an unbiased measure of volatility for the ‘open’ part of the day. The modified RV is unbiased even if 1-minute intra-day returns are used. Further, within a class of conditionally unbiased RVs we characterize the optimal RV in terms of the mean squared error criterion, and we determine the optimal combination of the RV and squared over-night return to form a measure of volatility for the whole day. We apply our results to the 30 stocks of the Dow Jones Industrial Average and an exchange-traded fund that tracks the S&P 500 index, and we construct five years of daily volatilities for these 31 equities. JEL Classification: C10; C22; C80.
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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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