ArticleEntropy (Basel, Switzerland)2024
Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models.
Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Entropy-Driven Volatility Prediction for ETF Quantitative Investment in the Chinese Stock Market: A Machine Learning Framework for Barbell Strategy Optimization.Entropy (Basel, Switzerland) · 2026Article
- Information-Theoretic ESG Index Direction Forecasting: A Complexity-Aware Framework.Entropy (Basel, Switzerland) · 2025Article
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Abstract
Volatility in financial markets refers to the variation in asset prices over time. High volatility indicates increased risk, making its evaluation essential for effective risk management. Various methods are used to assess volatility, with the standard deviation of log-returns being a common approach. However, this implicitly assumes that log-returns follow a Gaussian distribution, which is not always valid. In this paper, we explore the use of (differential) entropy to evaluate the volatility of financial log-returns. Estimation of entropy is obtained using a Gaussian mixture model to approximate the underlying density of log-returns. Following this modeling approach, popular risk measures such as Value at Risk and Expected Shortfall can also be computed. By integrating Gaussian mixture modeling and entropy into the analysis of log-returns, we aim to provide a more accurate and robust framework for assessing financial volatility and risk measures.
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