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Mutual information: a dependence measure for nonlinear time series

Author

Listed:
  • Andreia Dionisio

    (University of Evora)

  • Rui Menezes

    (ISCTE)

  • Diana A. Mendes

    (ISCTE)

Abstract

This paper investigates the possibility to analyse the structure of unconditional or conditional (and possibly nonlinear) dependence in financial returns without requiring the specification of mean-variance models or a theoretical probability distribution. The main goal of the paper is to show how mutual information can be used as a measure of dependence in financial time series. One major advantage of this approach resides precisely in its ability to account for nonlinear dependencies with no need to specify a theoretical probability distribution or use of a mean-variance model.

Suggested Citation

  • Andreia Dionisio & Rui Menezes & Diana A. Mendes, 2003. "Mutual information: a dependence measure for nonlinear time series," Econometrics 0311003, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0311003
    Note: Type of Document - Acrobat PDF; prepared on Win98; pages: 36; figures: 4. 36 pages, 4 figures, 21 tables
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0311/0311003.pdf
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    References listed on IDEAS

    as
    1. Bonanno, Giovanni & Lillo, Fabrizio & Mantegna, Rosario N., 2001. "Levels of complexity in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 16-27.
    2. Darbellay, Georges A & Wuertz, Diethelm, 2000. "The entropy as a tool for analysing statistical dependences in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 429-439.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. E. M. S. Ribeiro & G. A. Prataviera, 2014. "Information theoretic approach for accounting classification," Papers 1401.2954, arXiv.org, revised Sep 2014.
    2. Witold Orzeszko, 2010. "Measuring Nonlinear Serial Dependencies Using the Mutual Information Coefficient," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 97-106.
    3. Ribeiro, E.M.S. & Prataviera, G.A., 2014. "Information theoretic approach for accounting classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 651-660.
    4. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas Pérez-Iñigo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," Estudios Gerenciales, Universidad Icesi, November.
    5. Akimitsu Inoue, 2016. "Density estimation based on pointwise mutual information," Economics Bulletin, AccessEcon, vol. 36(2), pages 1138-1148.
    6. Andreia Teixeira Marques Dionísio, 2010. "On the globalization of stock markets: An application of VECM, SSA technique and mutual information to the G7?," CEFAGE-UE Working Papers 2010_06, University of Evora, CEFAGE-UE (Portugal).
    7. Menezes, Rui & Dionísio, Andreia & Hassani, Hossein, 2012. "On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 369-384.
    8. Chunxia, Yang & Xueshuai, Zhu & Luoluo, Jiang & Sen, Hu & He, Li, 2016. "Study on the contagion among American industries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 601-612.
    9. Kun Zhang & Laiwan Chan, 2009. "Efficient factor GARCH models and factor-DCC models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 71-91.

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    More about this item

    Keywords

    Mutual information; nonlinear dependence; market efficiency;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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