ArticleEntropy (Basel, Switzerland)2021
A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 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
- Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models.Entropy (Basel, Switzerland) · 2024Article
- The Cross-Sectional Intrinsic Entropy-A Comprehensive Stock Market Volatility Estimator.Entropy (Basel, Switzerland) · 2022Article
- Entropy Based Student'sEntropy (Basel, Switzerland) · 2021Article
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3 authors.
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Abstract
Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.
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