Realized kernels in practice: trades and quotes
Realized kernels in practice: trades and quotes
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DOI：
10.1111/j.1368-423x.2008.00275.x
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发表时间：
2009-01-01
影响因子：
1.9
通讯作者：
Shephard, N.
中科院分区：
文献类型：
--
作者：
Barndorff-Nielsen, O. E.;Hansen, P. Reinhard;Shephard, N.
实现核函数使用高频数据来估计单个股票价格的日波动率。它们可以应用于交易或报价数据。在这里，我们提供了我们建议如何在实践中实施它们的细节。我们比较了基于同一只股票的交易和报价数据的估计，发现了显著的一致性。我们识别了高频数据的一些特征，这些特征对实现的内核具有挑战性。它们是当数据中存在局部趋势时，在大约10分钟的时间内，价格和报价被推高或下降。这些可能与高容量有关。对此的一种解释是，它们是由于非平凡的流动性效应。
P>Realized kernels use high-frequency data to estimate daily volatility of individual stock prices. They can be applied to either trade or quote data. Here we provide the details of how we suggest implementing them in practice. We compare the estimates based on trade and quote data for the same stock and find a remarkable level of agreement.We identify some features of the high-frequency data, which are challenging for realized kernels. They are when there are local trends in the data, over periods of around 10 minutes, where the prices and quotes are driven up or down. These can be associated with high volumes. One explanation for this is that they are due to non-trivial liquidity effects.