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Bootstrapping High-Frequency Jump Tests
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DOI：10.1080/01621459.2018.1447485.png)
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摘要
En 中文
The main contribution of this article is to propose a bootstrap test for jumps based on functions of realized volatility and bipower variation. Bootstrap intraday returns are randomly generated from a mean zero Gaussian distribution with a variance given by a local measure of integrated volatility (which we denote by ). We first discuss a set of high-level conditions on such that any bootstrap test of this form has the correct asymptotic size and is alternative-consistent. We then provide a set of primitive conditions that justify the choice of a thresholding-based estimator for . Our cumulant expansions show that the bootstrap is unable to mimic the higher-order bias of the test statistic. We propose a modification of the original bootstrap test which contains an appropriate bias correction term and for which second-order asymptotic refinements are obtained.
Keyword:
Asymptotic refinements
Bias correction
Jump tests
Thresholding volatility bootstrap
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被引数:
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