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Approximate Entropy as an Irregularity Measure for Financial Data

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  • Steve Pincus

Abstract

The need to assess subtle, potentially exploitable changes in serial structure is paramount in the analysis of financial and econometric data. We demonstrate the utility of approximate entropy (ApEn), a model-independent measure of sequential irregularity, towards this goal, via several distinct applications, both empirical data and model-based. We also consider cross-ApEn, a related two-variable measure of asynchrony that provides a more robust and ubiquitous measure of bivariate correspondence than does correlation, and the resultant implications to diversification strategies. We provide analytic expressions for and statistical properties of ApEn, and compare ApEn to nonlinear (complexity) measures, correlation and spectral analyses, and other entropy measures.

Suggested Citation

  • Steve Pincus, 2008. "Approximate Entropy as an Irregularity Measure for Financial Data," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 329-362.
    • Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:329-362
    DOI: 10.1080/07474930801959750
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    Citations

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    Cited by:

    1. David E. Allen & Michael McAleer & Robert Powell & Abhay K. Singh, 2013. "A Non-Parametric and Entropy Based Analysis of the Relationship between the VIX and S&P 500," JRFM, MDPI, vol. 6(1), pages 1-25, October.
    2. Radhika Prosad Datta, 2023. "Regularity in forex returns during financial distress: Evidence from India," Papers 2308.04181, arXiv.org.
    3. Darrol J. Stanley & Levan Efremidze & Jannie Rossouw, 2017. "Entropy Risk Factor Model of Exchange Rate Prediction," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 8(3), pages 51-56, July.
    4. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
    5. Wine, Trevor, 2020. "A Matrix-Based Regularity Measure for Symbolic Sequences," OSF Preprints vpg8h, Center for Open Science.
    6. Jadhao, Gaurav & Chandra, Abhijeet, 2017. "Application of VIX and entropy indicators for portfolio rotation strategies," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1367-1371.
    7. Stephan Schwill, 2018. "Entropy Analysis of Financial Time Series," Papers 1807.09423, arXiv.org.
    8. Ataei, Masoud & Chen, Shengyuan & Yang, Zijiang & Peyghami, M. Reza, 2021. "Theory and applications of financial chaos index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    9. Efremidze, Levan & Stanley, Darrol J. & Kownatzki, Clemens, 2021. "Entropy trading strategies reveal inefficiencies in Japanese stock market," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 464-477.
    10. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.

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