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利用 Hawkes 过程预测高频订单流失衡

文章 arXiv papers · 作者: Aditya Nittur Anantha et al.
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总结

本文研究订单流失衡（OFI），这是一种基于买方和卖方事件不对称性的指标，与短期价格方向有关。研究使用 Hawkes 过程估计 OFI，同时对买卖双方订单流之间的滞后依赖性进行建模。作者还提出一种预测 OFI 近期分布的方法，以及比较多个模型预测结果的方法。

该方法应用于国家证券交易所的逐笔数据。在这项比较中，采用指数和型核函数的 Hawkes 过程在受测模型中给出了最佳预测。说明未列出对比模型、预测期限、评估指标或交易结果。预测 OFI 分布或可用于模型评估，但仅凭所述结果无法证明扣除执行成本后能够盈利，或该方法能够推广到其他交易场所。

核心观点

  • 买卖双方事件流可能存在不对称性和依赖关系，这种失衡可能与价格变动有关。
  • Hawkes 过程对买卖订单流之间的滞后依赖性建模，用于估计 OFI。
  • 本文预测近期 OFI 分布，并比较多个模型的预测结果。
  • 在所述交易所的逐笔数据中，采用指数和型 Hawkes 核函数的过程在受测模型中预测效果最佳。

标签

高频交易市场微观结构统计学股票
全文
# Forecasting High Frequency Order Flow Imbalance


# Forecasting High Frequency Order Flow Imbalance









Market information events are generated intermittently and disseminated at high speeds in real-time. Market participants consume this high-frequency data to build limit order books, representing the current bids and offers for a given asset. The arrival processes, or the order flow of bid and offer events, are asymmetric and possibly dependent on each other. The quantum and direction of this asymmetry are often associated with the direction of the traded price movement. The Order Flow Imbalance (OFI) is an indicator commonly used to estimate this asymmetry. This paper uses Hawkes processes to estimate the OFI while accounting for the lagged dependence in the order flow between bids and offers. Secondly, we develop a method to forecast the near-term distribution of the OFI, which can then be used to compare models for forecasting OFI. Thirdly, we propose a method to compare the forecasts of OFI for an arbitrarily large number of models. We apply the approach developed to tick data from the National Stock Exchange and observe that the Hawkes process modeled with a Sum of Exponential's kernel gives the best forecast among all competing models.

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。