基于金融超高频数据的二次协方差估计的流方法
Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data
摘要：
我们研究了二次协变估计中与内存大小相关的计算问题，同时考虑到金融超高频数据的具体情况。在多变量价格过程中，我们同时考虑了市场微观结构噪声和观测数据的非同步性。我们以二次型形式制定了多尺度、平顶实现核、非平顶实现核、预平均和调制实现协方差估计量，并将其带宽参数固定为常数值。这允许我们使用有限的内存进行操作，并将此估计公式化为流算法。我们比较了性能的估计与固定的带宽参数在模拟研究。我们发现，估计确保正半定性需要更高的带宽比估计没有这个约束。
英文摘要：
We investigate the computational issues related to the memory size in the estimation of quadratic covariation, taking into account the specifics of financial ultra-high-frequency data. In multivariate price processes, we consider both contamination by the market microstructure noise and the non-synchronicity of the observations. We formulate a multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in quadratic form and fix their bandwidth parameter at a constant value. This allows us to operate with limited memory and formulate this estimation as a streaming algorithm. We compare the performance of the estimators with fixed bandwidth parameter in a simulation study. We find that the estimators ensuring positive semidefiniteness require much higher bandwidth than the estimators without this constraint.
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