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The main contribution of this article is to propose bootstrap methods for realized volatility-like estimators defined on pre-averaged returns. In particular, we focus on the pre-averaged realized volatility estimator proposed by Podolskij and Vetter (2009). This statistic can be written (up to a bias correction term) as the (scaled) sum of squared preaveraged returns, where the pre-averaging is done over all possible nonoverlapping blocks of consecutive observations. Pre-averaging reduces the influence of the noise and allows for realized volatility estimation on the pre-averaged returns. The nonoverlapping nature of the pre-averaged returns implies that these are asymptotically uncorrelated, but possibly heteroskedastic. This motivates the application of the wild bootstrap in this context. We provide a proof of the firstorder asymptotic validity of this method for percentile and percentile-t intervals. Our Monte Carlo simulations show that the wild bootstrap can improve the finite sample properties of the existing first-order asymptotic theory provided we choose the external random variable appropriately. We use empirical work to illustrate its use in practice.
Original language | English |
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Article number | nbu011 |
Journal | Journal of Financial Econometrics |
Volume | 12 |
Issue | 4 |
Pages (from-to) | 679-707 |
Number of pages | 29 |
ISSN | 1479-8409 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
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