The extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a financial market remains an unanswered concern with regards to market models. Here we consider a 10-variate Hawkes process with simple rules to simulate common order types which are submitted to a matching engine. Hawkes processes can be used to model the time and order of events, and how these events relate to each other. However, they provide a freedom with regards to implementation mechanics relating to the prices and volumes of injected orders. This allows us to consider a reference Hawkes model and two additional models which have rules that change the behaviour of limit orders. The resulting trade and quote data from the simulations are then calibrated and compared with the original order generating process to determine the extent with which implementation rules can distort model parameters. Evidence from validation and hypothesis tests suggest that the true model specification can be significantly distorted by market mechanics, and that practical considerations not directly due to model specification can be important with regards to model identification within an inherently asynchronous trading environment.
翻译:匹配引擎能够对金融市场基本订单提交和管理过程的建模造成云雾的程度,对于市场模型来说,仍是一个未受关注的问题。在这里,我们考虑的是10个有10个变量的霍克斯进程,其中含有模拟共同订单类型的简单规则,并提交给匹配引擎。可以使用霍克斯进程来模拟事件的时间和顺序,以及这些事件之间的相互联系。然而,它们为与注入订单的价格和数量有关的执行机械提供了自由。这使我们能够考虑一个参考霍克斯模式和另外两个模式,这些模式有改变限制订单行为的规则。随后对模拟中产生的贸易和引用数据进行了校准,并与原始生成程序进行比较,以确定执行规则在多大程度上扭曲了模型参数。验证和假设试验的证据表明,真正的模型规格可以被市场机械明显扭曲,而并非直接与模型规格无关的实际考虑对于在固有的不连贯的贸易环境中进行模型识别十分重要。