AI 中文总结
针对高频金融市场极端价格变动检测难题，提出基于波动率感知的方法，利用比特币限价订单簿数据，通过扩展目标公式、采用XGBoost模型及相关验证评估，显著提升检测罕见事件能力，凸显目标设计在金融机器学习中的关键作用。
AI 中文摘要
预测高频金融市场中的极端价格变动具有挑战性，因其具有非平稳性、重尾收益分布和严重的类别不平衡。传统方法难以检测罕见但有影响的事件。本研究提出一种基于波动率感知的方法，利用高频比特币限价订单簿数据进行极端事件检测。通过扩展目标公式纳入未来大回报和高波动状态，重新定义增加了信息样本比例。使用基于树的模型（XGBoost）及时间序列交叉验证和不平衡感知评估，该方法的精确召回率AUC约为0.40，显著优于基线公式。结果表明目标设计在金融机器学习中起关键作用，该方法为高频加密货币市场极端事件检测提供了更有效和现实的框架。
英文摘要
Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance. In particular, rare but impactful events are often difficult to detect using conventional modeling approaches, which typically treat extreme movements as isolated observations. This study proposes a volatility-aware approach for extreme event detection using high-frequency Bitcoin limit order book (LOB) data. Motivated by empirical evidence of volatility clustering, the target formulation is extended to incorporate both large future returns and high-volatility regimes. This redefinition increases the proportion of informative samples and aligns the learning objective with the underlying market dynamics. Using a tree-based model (XGBoost) with time-series cross-validation and imbalance-aware evaluation, the proposed method achieves a Precision-Recall AUC of approximately 0.40, significantly outperforming the baseline formulation with a PR-AUC of around 0.06. This represents more than a sixfold improvement in detecting rare events. The results highlight that target design plays a critical role in financial machine learning, often exceeding the impact of model complexity. By incorporating volatility structure into the labeling process, the proposed approach provides a more effective and realistic framework for extreme event detection in high-frequency cryptocurrency markets.
Comments7 pages, 3 figures. Preprint
