论文标题
使用财务超高频率数据进行二次协变估计的流媒体方法
Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data
论文作者
论文摘要
我们研究了财务超高频率数据的细节，研究了与二次协变量估计中与记忆大小相关的计算问题。在多元价格过程中，我们考虑了市场微观结构噪声的污染和观测值的非同步性。我们以二次形式制定了多尺度的，平面实现的内核，非平台实现的内核，预定和调制实现的协方差估计器，并以恒定值修复其带宽参数。这使我们能够以有限的内存操作，并将此估计作为流算法制定。在模拟研究中，我们将估计器的性能与固定带宽参数进行了比较。我们发现，比没有这种约束的估计器要确保积极的半足以带宽所需的估计器所需的带宽要高得多。
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.
