Detecting Anomalies in WTI Crude Oil Returns Using Statistical and Machine Learning Methods

Music City Center 

Crude oil price fluctuations significantly impact global economies, financial markets, and energy policies. Detecting anomalies in West Texas Intermediate (WTI) crude oil returns is essential for identifying market shocks and enhancing risk management strategies. This study presents a hybrid anomaly detection framework that integrates statistical techniques (Z-score, Bollinger Bands, GARCH) with machine learning models (Isolation Forest, DBSCAN, Autoencoders). Using daily WTI returns from 2014 to 2024, the analysis identifies both extreme return spikes and complex nonlinear deviations.
The results show that Bollinger Bands and GARCH methods detect a higher number of anomalies, reflecting sensitivity to volatility, while machine learning techniques such as Isolation Forest and Autoencoders identify subtler, nonlinear patterns. A total of 26 consensus anomalies, detected by at least three methods, highlight major market disruptions, which was captured by all six models.
This research demonstrates that combining statistical and machine learning approaches enhances anomaly detection by leveraging their complementary strengths. The findings offer valuable insights for financial risk assessment, market surveillance, and economic policy-making, contributing to more robust decision-making in energy and financial markets.

Anomaly Detection

WTI Crude Oil Returns

Machine Learning

Financial Risk Management

Volatility Clustering

Isolation Forest