This paper proposes a forecast-centric adaptive learning model that engages with the past studies on the order book and high-frequency data, with applications to hypothesis testing. In line with the past literature, we produce brackets of summaries of statistics from the high-frequency bid and ask data in the CSI 300 Index Futures market and aim to forecast the one-step-ahead prices. Traditional time series issues, e.g. ARIMA order selection, stationarity, together with potential financial applications are covered in the exploratory data analysis, which pave paths to the adaptive learning model. By designing and running the learning model, we found it to perform well compared to the top fixed models, and some could improve the forecasting accuracy by being more stable and resilient to non-stationarity. Applications to hypothesis testing are shown with a rolling window, and further potential applications to finance and statistics are outlined.
翻译:本文提出一个预测中心适应性学习模式,与过去对订单簿和高频数据的研究相结合,并应用假设测试。根据以往的文献,我们编制高频出价统计摘要的括号,并在CSI 300指数期货市场上询问数据,目的是预测一步骤价格。传统的时间序列问题,如ARIMA订单选择、静态性以及潜在的财务应用都包含在探索性数据分析中,这为适应性学习模式铺平了道路。通过设计和运行该学习模式,我们发现它与顶级固定模型相比表现良好,有些可以提高预测的准确性,办法是更加稳定,更能适应非静态。对假设测试的应用以滚动窗口的形式展示,并概述了对融资和统计的进一步潜在应用。