英文标题：
《Queue-reactive Hawkes models for the order flow》
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作者：
Peng Wu, Marcello Rambaldi, Jean-Fran\\c{c}ois Muzy, Emmanuel Bacry
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最新提交年份：
2019
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英文摘要：
  In this work we introduce two variants of multivariate Hawkes models with an explicit dependency on various queue sizes aimed at modeling the stochastic time evolution of a limit order book. The models we propose thus integrate the influence of both the current book state and the past order flow. The first variant considers the flow of order arrivals at a specific price level as independent from the other one and describes this flow by adding a Hawkes component to the arrival rates provided by the continuous time Markov \"Queue Reactive\" model of Huang et al. Empirical calibration using Level-I order book data from Eurex future assets (Bund and DAX) show that the Hawkes term dramatically improves the pure \"Queue-Reactive\" model not only for the description of the order flow properties (as e.g. the statistics of inter-event times) but also with respect to the shape of the queue distributions. The second variant we introduce describes the joint dynamics of all events occurring at best bid and ask sides of some order book during a trading day. This model can be considered as a queue dependent extension of the multivariate Hawkes order-book model of Bacry et al. We provide an explicit way to calibrate this model either with a Maximum-Likelihood method or with a Least-Square approach. Empirical estimation from Bund and DAX level-I order book data allow us to recover the main features of Hawkes interactions uncovered in Bacry et al. but also to unveil their joint dependence on bid and ask queue sizes. We notably find that while the market order or mid-price changes rates can mainly be functions on the volume imbalance this is not the case for the arrival rate of limit or cancel orders. Our findings also allows us to clearly bring to light various features that distinguish small and large tick assets.
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中文摘要：
在这项工作中，我们引入了两种变量的多变量霍克斯模型，该模型对不同的队列大小具有明确的依赖性，旨在建模极限订单簿的随机时间演化。因此，我们提出的模型综合了当前图书状态和过去订单流的影响。第一个变量认为特定价格水平下的订单到达流独立于另一个价格水平，并通过在Huang等人的连续时间马尔可夫“队列反应”模型提供的到达率中添加Hawkes分量来描述该流。使用Eurex future assets（Bund和DAX）的一级订单簿数据进行的经验校准表明，Hawkes项显著改进了纯“队列反应”模型，不仅用于描述订单流属性（例如，事件间时间的统计信息），还用于描述队列分布的形状。我们介绍的第二种变体描述了在一个交易日内，在某些订单的最佳买卖双方发生的所有事件的联合动态。该模型可被视为Bacry等人的多元Hawkes订货簿模型的队列相关扩展。我们提供了一种明确的方法，用最大似然法或最小二乘法来校准该模型。通过Bund和DAX一级订单数据的经验估计，我们可以恢复Bacry等人发现的Hawkes相互作用的主要特征，但也可以揭示它们对出价和询问队列大小的共同依赖性。我们特别发现，虽然市场订单或中间价格变化率主要是数量不平衡的函数，但限价或取消订单的到达率却不是这样。我们的发现还使我们能够清楚地揭示区分小型和大型蜱类资产的各种特征。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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