多维时变价格过期问题研究

Haibin Zhu, Zhi Liu
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引用次数: 0

摘要

价格滞后性指的是价格动态中存在零收益的程度。Bandi等人(2020a)指出,在高频数据集中,零收益的比例可能很高。将价格滞后性也视为一个动态系统,本文扩展了Bandi et al. (2020b)的框架,研究了资产之间的特异性价格滞后性和系统性价格滞后性的统计推断,其中系统性价格滞后性表征了共同零收益存在的概率。在新框架下,我们提出了特殊价格滞后和系统价格滞后的一致性估计,并建立了它们的分布理论。此外,我们还提出了一种可行的非参数检验方法来检验系统价格的稳定性。所有的方法都是基于高频观测,也就是说,我们要求观测间隔缩小到零。最后,我们在各种场景下进行了模拟研究,以评估所提出方法的有限样本性能,并为所提出的理论提供了经验说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Multi-Dimensional Time-Varying Price Staleness
The price staleness is referred to as the extent of the presence of the zero returns in the price dynamics. The proportion of the zero returns could be high in the high-frequency data sets, as pointed by Bandi et al. (2020a). Considering the price staleness as a dynamic system too, in this paper, we extend the framework of Bandi et al. (2020b) and study the statistical inference of the idiosyncratic price staleness and systematic price staleness between assets, where the systematic price staleness characterizes the probability of the presence of com- mon zero returns. We propose consistent estimators for both the idiosyncratic and systematic price stalenesses under the new framework, and their distributional theory are established. Moreover, we develop a feasible nonparametric test for the constancy of the systematic price staleness. All of the methodologies are based on the high-frequency observations, namely, we require the observing intervals shrink to zero. Finally, we conduct simulation studies under various scenarios to assess the finite sample performance of the proposed approaches, and provide an empirical illustration for the proposed theory.
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