Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets
2026-07-20 • Machine Learning
Machine Learning
AI summaryⓘ
The authors tackle the tough problem of predicting big, sudden price changes in fast-moving Bitcoin markets. They found that by considering times when the market is volatile (up and down a lot) along with big price moves, their method uses more useful information to learn from. They used a tree-based machine learning model and showed it detects rare extreme events much better than older methods, improving detection six times. Their main point is that carefully defining what counts as an extreme event matters more than just using complex models.
high-frequency tradinglimit order bookvolatility clusteringextreme event detectionmachine learningXGBoostPrecision-Recall AUCclass imbalancecryptocurrency marketstime-series cross-validation
Authors
Maorufa Zaman, Haris Md Sahed
Abstract
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.