论文标题
从订单到价格：限制订单簿的随机描述到预测日内收益
From orders to prices: A stochastic description of the limit order book to forecast intraday returns
论文作者
论文摘要
我们提出了一个微观模型，以描述限制顺序簿（LOB）中基本事件的动力学：顺序到达和取消。它基于操作员代数用于单个订单，并描述了它们对LOB的影响。模型输入是从交易者的个人行为中产生的到达和取消率分布，我们显示了LOB动态的价格和流动性。在一项模拟研究中，我们说明了模型如何工作，并强调了其对市场参与者集体行为的假设的敏感性。从经验上讲，我们在Xetra的LOB快照上测试了该模型，估计几个线性化模型规范并进行样本外预测。基于同时信息的样本结果结果表明，我们的模型非常很好地描述了回报，从而导致调整后的$ r^2 $ r^2 $的大约80％。在更现实的环境中，只有过去的信息进入模型，我们观察到调整后的$ r^2 $左右约15％。可以预测（样本外）的下一个回报方向，其准确度低于10分钟以下的时间范围75％。平均而言，我们获得的RMSPE比文献中记录的值低10倍。
We propose a microscopic model to describe the dynamics of the fundamental events in the limit order book (LOB): order arrivals and cancellations. It is based on an operator algebra for individual orders and describes their effect on the LOB. The model inputs are arrival and cancellation rate distributions that emerge from individual behavior of traders, and we show how prices and liquidity arise from the LOB dynamics. In a simulation study we illustrate how the model works and highlight its sensitivity with respect to assumptions regarding the collective behavior of market participants. Empirically, we test the model on a LOB snapshot of XETRA, estimate several linearized model specifications, and conduct in- and out-of-sample forecasts.The in-sample results based on contemporaneous information suggest that our model describes returns very well, resulting in an adjusted $R^2$ of roughly 80%. In the more realistic setting where only past information enters the model, we observe an adjusted $R^2$ around 15%. The direction of the next return can be predicted (out-of-sample) with an accuracy above 75% for time horizons below 10 minutes. On average, we obtain an RMSPE that is 10 times lower than values documented in the literature.
