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arXiv 1708.02411q-fin.TR
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来自线性模型的非线性价格影响

Nonlinear price impact from linear models

Felix Patzelt, Jean-Philippe Bouchaud

摘要:

交易对资产价格的影响是学术界、监管机构和从业者等市场动态的一个关键方面。最近,对于所有日内尺度上的价格影响汇总,观察到了普遍和高度非线性的主曲线[1]。在这里,我们调查如何以及这些曲线,它们的缩放,和潜在的回报动态捕捉线性“传播”模型。我们发现,分类的交易价格变化与非价格变化可以解释价格影响的非线性和短期回报动态到一个非常高的程度。除了订单符号历史之外,变化指示器提供的解释能力随着最小价格变动单位的增加而增加。为了获得这些结果,几个长期存在的模型校准和测试的技术问题得到解决。我们提出了新的谱估计两点和三点互相关,消除了以前使用的近似。我们还展示了校准何时是无偏的,以及如何准确地揭示以前被忽视的偏差。因此,我们的研究结果有助于理解最近的实证结果和流行的一类影响模型的属性显着。

英文摘要:

The impact of trades on asset prices is a crucial aspect of market dynamics for academics, regulators and practitioners alike. Recently, universal and highly nonlinear master curves were observed for price impacts aggregated on all intra-day scales [1]. Here we investigate how well these curves, their scaling, and the underlying return dynamics are captured by linear "propagator" models. We find that the classification of trades as price-changing versus non-price-changing can explain the price impact nonlinearities and short-term return dynamics to a very high degree. The explanatory power provided by the change indicator in addition to the order sign history increases with increasing tick size. To obtain these results, several long-standing technical issues for model calibration and -testing are addressed. We present new spectral estimators for two- and three-point cross-correlations, removing the need for previously used approximations. We also show when calibration is unbiased and how to accurately reveal previously overlooked biases. Therefore, our results contribute significantly to understanding both recent empirical results and the properties of a popular class of impact models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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