IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpem/0311003.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0311/0311003.pdf
    Download Restriction: no
    ---><---

    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

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Rui Menezes & Andreia Dionísio & Hossein Hassanic, 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Matesanz & Guillermo Ortega, 2014. "Network analysis of exchange data: interdependence drives crisis contagion," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 1835-1851, July.
    2. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    3. Javier Morales & V'ictor Tercero & Fernando Camacho & Eduardo Cordero & Luis L'opez & F-Javier Almaguer, 2014. "Trend and Fractality Assessment of Mexico's Stock Exchange," Papers 1411.3399, arXiv.org.
    4. Gábor Dávid Kiss & Tamás Schuszter, 2015. "The Euro Crisis and Contagion among Central and Eastern European Currencies: Recommendations for Avoiding Lending in a Safe Haven Currency such as CHF," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(6), pages 678-698.
    5. Bertrand M. Roehner, 2004. "Stock markets are not what we think they are: the key roles of cross-ownership and corporate treasury stock," Papers cond-mat/0406704, arXiv.org.
    6. Arthur Matsuo Yamashita Rios de Sousa & Hideki Takayasu & Misako Takayasu, 2017. "Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.
    7. Arnold Polanski & Evarist Stoja & Ching‐Wai (Jeremy) Chiu, 2021. "Tail risk interdependence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5499-5511, October.
    8. Yuri A. Dubnov & Alexandr V. Boulytchev, 2023. "Accelerated Maximum Entropy Method for Time Series Models Estimation," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    9. 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.
    10. Civitarese, Jamil, 2016. "Volatility and correlation-based systemic risk measures in the US market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 55-67.
    11. Chen, Wang & Wei, Yu & Lang, Qiaoqi & Lin, Yu & Liu, Maojuan, 2014. "Financial market volatility and contagion effect: A copula–multifractal volatility approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 289-300.
    12. Brida, Juan Gabriel & Risso, Wiston Adrián, 2008. "Multidimensional minimal spanning tree: The Dow Jones case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5205-5210.
    13. Andreia Dionisio & Rui Menezes & Diana A. Mendes & Jacinto Vidigal da Silva, 2004. "Linear and nonlinear models for the analysis of the relationship between stock market prices and macroeconomic and financial factors," Econometrics 0411018, University Library of Munich, Germany.
    14. N. Mondal & V. Singh & S. Ahmed, 2012. "Entropy-Based Approach for Assessing Natural Recharge in Unconfined Aquifers from Southern India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(9), pages 2715-2732, July.
    15. Subhamitra Patra & Gourishankar S. Hiremath, 2022. "An Entropy Approach to Measure the Dynamic Stock Market Efficiency," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 337-377, June.
    16. Aktham Maghyereh & Hussein Abdoh, 2022. "Global financial crisis versus COVID‐19: Evidence from sentiment analysis," International Finance, Wiley Blackwell, vol. 25(2), pages 218-248, August.
    17. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    18. Ferreira, Paulo & Dionísio, Andreia, 2016. "How long is the memory of the US stock market?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 502-506.
    19. Witold Orzeszko, 2010. "Measuring Nonlinear Serial Dependencies Using the Mutual Information Coefficient," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 97-106.
    20. Yin, Yi & Shang, Pengjian & Ahn, Andrew C. & Peng, Chung-Kang, 2019. "Multiscale joint permutation entropy for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 388-402.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpem:0311003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.