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欧洲加密货币交易所交易产品的日内异常检测

文章 arXiv papers · 作者: Julia Kończal et al.
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总结

本研究考察在 Xetra 和 Nasdaq Stockholm 上市的比特币和以太坊交易所交易产品的日内异常，使用从2024年1月至2025年12月的一分钟 K 线。基准方法通过对左尾超额拟合广义帕累托分布来识别极端负收益。作者又加入三个二元指标：不同交易场所间的价格偏离、之后十个活跃 K 线内几乎没有恢复的极端下跌，以及发生在短期正向动量之后的极端下跌。

每类异常所占 K 线比例均低于1%。Mann-Whitney U 检验发现，与普通 K 线相比，异常观测的有效价差、流动性相关比率和订单流失衡程度更高。四种分类器提前一个 K 线预测异常，报告的AUC-ROC最高达0.82；总体而言，置换重要性显示短期波动率和回撤指标比微观结构变量更有预测作用。这些发现涉及异常分类及相关市场状况，并非经过测试的交易策略。摘录没有说明交易成本，也未说明根据预测采取行动是否能够盈利；结果仅限于所述产品、交易场所、时期和 K 线频率。

核心观点

  • 基准方法使用广义帕累托分布标记极端负收益 K 线。
  • 另外三个指标分别捕捉跨交易场所的价格偏离、缺乏反弹，以及正向动量后的反转。
  • 异常 K 线与较宽的有效价差、较高的流动性比率和更强的订单流失衡相关。
  • 四种分类器显示可提前一个 K 线预测，AUC-ROC最高达0.82。
  • 总体而言，短期波动率和回撤指标比微观结构变量更有预测作用。

标签

加密资产市场微观结构统计学机器学习波动率
全文
# Anomaly detection in European cryptocurrency exchange-traded products


# Anomaly detection in European cryptocurrency exchange-traded products









Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow imbalances than non-anomalous bars. Furthermore, employing an out-of-sample prediction methodology with four classifiers - random forest, logistic regression, extreme gradient boosting, and light gradient boosting machine - shows that all four anomaly types are predictable one bar ahead, with AUC-ROC values of up to 0.82. Permutation importance indicates that short-term volatility and drawdown measures are generally more useful for prediction than microstructure variables.

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。