Leveraging Sample Entropy for Enhanced Volatility Measurement and Prediction in International Oil Price Returns

ArXiv ID: 2312.12788 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Sample Entropy, Volatility prediction, Oil prices, Time series analysis, Machine learning, Commodities (Oil)

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper applies a well-defined metric (Sample Entropy) with established formulas rather than developing new complex mathematics, but implements it across a comprehensive 27-year dataset using multiple ML techniques (SVM, KNN) and time series regression with specific ARIMA(4,1,3) parameters, demonstrating practical implementation and performance evaluation.

  flowchart TD
    A["Research Goal: Apply Sample Entropy<br>to predict oil price volatility"] --> B{"Data Preparation"}
    
    B --> C["Dataset: 27 Years<br>International Oil Prices 1986-2023"]
    C --> D["Feature Engineering"]
    D --> E["Compute Sample Entropy<br>Standard Deviation<br>Lagged Returns"]
    
    E --> F{"Computational Models"}
    
    F --> G["Time Series Regression"]
    F --> H["Machine Learning Models"]
    
    G --> I["Findings:<br>SampEn effectively predicts<br>traditional volatility"]
    H --> J["Findings:<br>ML outperforms regression<br>during financial crises"]
    
    I --> K["Final Outcome:<br>SampEn is a valuable tool<br>for risk assessment &<br>oil price investment decisions"]
    J --> K