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连续双向拍卖的随机模型

文章 arXiv papers · 作者: Eric Smith et al.
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总结

本文构建连续双向拍卖的微观统计模型。连续双向拍卖是许多金融市场采用的订单撮合机制。模型假设随机订单流独立同分布，并通过模拟、量纲分析和平均场近似分析由此产生的市场行为。订单和撤单量、典型订单规模及最小报价单位等输入均可直接测量，因此模型无需自由参数。

模型预测波动率、订单簿深度、买卖价差、价格冲击以及订单成交的概率和时间等特征。在许多情况下，模型认为订单规模的离散程度比最小报价单位更能影响市场行为，并对价格冲击的凹形曲线提供了一种解释。这些理论预测建立在简化假设之上：随机且非策略性的订单流可能无法代表真实参与者。研究将零智能模型视为检验市场结构如何影响结果的有用工具，而非对市场行为的完整描述。

核心观点

  • 该模型用独立随机订单流描述连续双向拍卖。
  • 可测量的订单流和订单簿数据决定模型预测，无需自由参数。
  • 模型预测波动率、深度、价差、价格冲击和订单成交情况。
  • 订单规模的离散程度往往比最小报价单位更影响市场行为。
  • 模型解释了凹形价格冲击，但依赖对订单流的简化假设。

标签

市场微观结构统计学交易执行
全文
# Statistical theory of the continuous double auction


# Statistical theory of the continuous double auction









Most modern financial markets use a continuous double auction mechanism to store and match orders and facilitate trading. In this paper we develop a microscopic dynamical statistical model for the continuous double auction under the assumption of IID random order flow, and analyze it using simulation, dimensional analysis, and theoretical tools based on mean field approximations. The model makes testable predictions for basic properties of markets, such as price volatility, the depth of stored supply and demand vs. price, the bid-ask spread, the price impact function, and the time and probability of filling orders. These predictions are based on properties of order flow and the limit order book, such as share volume of market and limit orders, cancellations, typical order size, and tick size. Because these quantities can all be measured directly there are no free parameters. We show that the order size, which can be cast as a nondimensional granularity parameter, is in most cases a more significant determinant of market behavior than tick size. We also provide an explanation for the observed highly concave nature of the price impact function. On a broader level, this work suggests how stochastic models based on zero-intelligence agents may be useful to probe the structure of market institutions. Like the model of perfect rationality, a stochastic-zero intelligence model can be used to make strong predictions based on a compact set of assumptions, even if these assumptions are not fully believable.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。