Linear Models for the Impact of Order Flow on Prices I. Propagators: Transient vs. History Dependent Impact

Linear Models for the Impact of Order Flow on Prices I. Propagators: Transient vs. History Dependent Impact
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订单流对价格影响的线性模型 I. 传播者:瞬时影响与历史相关影响

DOI:
10.2139/ssrn.2770352
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发表时间:
2016
期刊:
Capital Markets: Market Microstructure eJournal
影响因子:
--
通讯作者:
B. Tóth
B. Tóth
中科院分区:
--
文献类型:
--
作者:
D. Taranto;G. Bormetti;J. Bouchaud;F. Lillo;B. Tóth

文献摘要

被引文献

相似文献

市场影响是金融市场研究中的一个重要概念,迄今为止,文献中已经提出了几个模型。瞬态影响模型(TIM)假定高频时间尺度的价格是过去执行的市场订单的符号的线性组合,由所谓的传播函数加权。另一种描述-历史相关影响模型(HDIM)-假设实现的订单符号与其预期水平之间的偏差会线性和永久地影响价格。这两个模型,但是,应该扩展,因为价格是先验的影响,不仅由过去的订单流,但也由过去实现的回报本身。在本文中,我们提出了一个两事件框架,其中价格变化和非价格变化事件被分开考虑。双事件传播模型提供了一个显着的改进的市场影响的描述,特别是对于大的股票,价格变化的事件是非常罕见的,非常翔实的。具体来说,扩展的方法捕捉了过去的回报和随后的订单流,这是在一个事件模型中丢失的过度反相关。我们的研究结果证明了HDIM的上级性能,尽管与TIM相比,HDIM的性能仅在较小的相对范围内。这有点令人惊讶,因为HDIM在理论上有很好的基础,而TIM严格来说是不一致的。
Market impact is a key concept in the study of financial markets and several models have been proposed in the literature so far. The Transient Impact Model (TIM) posits that the price at high frequency time scales is a linear combination of the signs of the past executed market orders, weighted by a so-called propagator function. An alternative description -- the History Dependent Impact Model (HDIM) -- assumes that the deviation between the realised order sign and its expected level impacts the price linearly and permanently. The two models, however, should be extended since prices are a priori influenced not only by the past order flow, but also by the past realisation of returns themselves. In this paper, we propose a two-event framework, where price-changing and non price-changing events are considered separately. Two-event propagator models provide a remarkable improvement of the description of the market impact, especially for large tick stocks, where the events of price changes are very rare and very informative. Specifically the extended approach captures the excess anti-correlation between past returns and subsequent order flow which is missing in one-event models. Our results document the superior performances of the HDIMs even though only in minor relative terms compared to TIMs. This is somewhat surprising, because HDIMs are well grounded theoretically, while TIMs are, strictly speaking, inconsistent.