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英文文献：Asymptotic theory of range-based multipower variation-基于范围的多幂变差的渐近理论
英文文献作者：Kim Christensen,Mark Podolskij
英文文献摘要：
In this paper, we present a realised range-based multipower variation theory, which can be used to estimate return variation and draw jump-robust inference about the diffusive volatility component, when a high-frequency record of asset prices is available. The standard range-statistic – routinely used in financial economics to estimate the variance of securities prices – is shown to be biased when the price process contains jumps. We outline how the new theory can be applied to remove this bias by constructing a hybrid range-based estimator. Our asymptotic theory also reveals that when high-frequency data are sparsely sampled, as is often done in practice due to the presence of microstructure noise, the range-based multipower variations can produce significant efficiency gains over comparable subsampled returnbased estimators. The analysis is supported by a simulation study and we illustrate the practical use of our framework on some recent TAQ equity data.
在本文中，我们提出了一个实现的基于范围的多幂变异理论，它可以用来估计收益变异，并提出关于扩散波动分量的跳强推论，当一个高频的资产价格记录是可用的。当价格过程包含跳跃时，标准区间统计——金融经济学中通常用于估计证券价格的方差——就会出现偏差。我们概述了如何应用新的理论，以消除这种偏差，通过构造一个混合范围的估计。我们的渐近理论还表明，当高频数据稀疏采样时，由于微结构噪声的存在，在实践中经常这样做，基于范围的多功率变化可以产生显著的效率增益比可比的下采样的基于回报的估计器。该分析由一个模拟研究支持，我们说明了我们的框架在一些最近的TAQ权益数据的实际使用。
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