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Nonparametric threshold estimation of spot volatility based on high-frequency data for time-dependent diffusion models with jumps

机译:基于具有跳跃的时间依赖扩散模型的高频数据的光斑波动率的非参数阈值估计

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We construct a spot volatility kernel estimator of time-dependent diffusion models with jumps. Instead of idiomatic intraday return over an observation interval, in the proposed estimator, we use intraday range. Since the range represents the maximum difference among all observations within an interval, all data are used, and no information is lost. By setting a reasonable threshold and making the range not greater than it we effectively eliminate the negative effect of jump on volatility estimation. In this paper, we also prove the consistency and asymptotic normality of the estimator and testify its higher accuracy.
机译:我们构建具有跳跃的时间依赖扩散模型的点波动次估计器。 在建议的估计者中,我们使用盘中范围而不是惯用的内部内返回观察间隔。 由于该范围表示间隔内所有观察的最大差异,因此使用所有数据,并且没有丢失信息。 通过设定合理的阈值并使不大于它的范围,我们有效地消除了跳跃对波动率估计的负面影响。 在本文中,我们还证明了估算者的一致性和渐近常态,并证明了其更高的准确性。

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