A ROBUST NEIGHBORHOOD TRUNCATION APPROACH TO ESTIMATION OF INTEGRATED QUARTICITY
A ROBUST NEIGHBORHOOD TRUNCATION APPROACH TO ESTIMATION OF INTEGRATED QUARTICITY
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综合质量估计的鲁棒邻域截断法
DOI：
10.1017/s026646661300011x
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发表时间：
2012
影响因子：
0.8
通讯作者：
E. Schaumburg
中科院分区：
文献类型：
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作者：
T. Andersen;Dobrislav Dobrev;E. Schaumburg
我们提供了第一次深入研究基于高频数据的综合四度（IQ）的稳健估计。智商是关键的成分，使推理的波动性和跳跃的存在，在金融时间序列，因此在应用中相当感兴趣。我们记录了常见的数据不完善所带来的IQ估计的重大经验挑战，并提出了三种互补的方法来改善基于IQ的推理。首先，我们表明，许多常见的偏差从跳跃扩散零可以处理一种新的过滤方案，概括截断个人返回截断返回块上的任意泛函。其次，我们提出了一个新的家庭的有效的鲁棒邻域截断（RNT）估计的综合功率变化的顺序统计量的基础上的一组无偏局部功率变化估计块的回报。第三，我们发现，基于比率的推理，最初提出的Barndorff-Nielsen和Shephard（2002年，应用计量经济学杂志17，457-477），在面对经常发生的数据不完善，具有理想的鲁棒性，因此非常适合于实证应用。我们确认，建议的过滤方案和RNT估计执行良好，在我们广泛的模拟设计和应用程序中的个人道琼斯30股票。
We provide a first in-depth look at robust estimation of integrated quarticity (IQ) based on high-frequency data. IQ is the key ingredient enabling inference about volatility and the presence of jumps in financial time series and is thus of considerable interest in applications. We document the significant empirical challenges for IQ estimation posed by commonly encountered data imperfections and set forth three complementary approaches for improving IQ-based inference. First, we show that many common deviations from the jump-diffusive null can be dealt with by a novel filtering scheme that generalizes truncation of individual returns to truncation of arbitrary functionals on return blocks. Second, we propose a new family of efficient robust neighborhood truncation (RNT) estimators for integrated power variation based on order statistics of a set of unbiased local power variation estimators on a block of returns. Third, we find that ratio-based inference, originally proposed in this context by Barndorff-Nielsen and Shephard (2002, Journal of Applied Econometrics 17, 457–477), has desirable robustness properties in the face of regularly occurring data imperfections and thus is well suited for empirical applications. We confirm that the proposed filtering scheme and the RNT estimators perform well in our extensive simulation designs and in an application to the individual Dow Jones 30 stocks.