  This paper explores the application of Sample Entropy (SampEn) as a
sophisticated tool for quantifying and predicting volatility in international
oil price returns. SampEn, known for its ability to capture underlying patterns
and predict periods of heightened volatility, is compared with traditional
measures like standard deviation. The study utilizes a comprehensive dataset
spanning 27 years (1986-2023) and employs both time series regression and
machine learning methods. Results indicate SampEn's efficacy in predicting
traditional volatility measures, with machine learning algorithms outperforming
standard regression techniques during financial crises. The findings underscore
SampEn's potential as a valuable tool for risk assessment and decision-making
in the realm of oil price investments.
