[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1801.06896.html
   My bibliography  Save this paper

Ranking Causal Influence of Financial Markets via Directed Information Graphs

Author

Listed:
  • Theo Diamandis
  • Yonathan Murin
  • Andrea Goldsmith
Abstract
A non-parametric method for ranking stock indices according to their mutual causal influences is presented. Under the assumption that indices reflect the underlying economy of a country, such a ranking indicates which countries exert the most economic influence in an examined subset of the global economy. The proposed method represents the indices as nodes in a directed graph, where the edges' weights are estimates of the pair-wise causal influences, quantified using the directed information functional. This method facilitates using a relatively small number of samples from each index. The indices are then ranked according to their net-flow in the estimated graph (sum of the incoming weights subtracted from the sum of outgoing weights). Daily and minute-by-minute data from nine indices (three from Asia, three from Europe and three from the US) were analyzed. The analysis of daily data indicates that the US indices are the most influential, which is consistent with intuition that the indices representing larger economies usually exert more influence. Yet, it is also shown that an index representing a small economy can strongly influence an index representing a large economy if the smaller economy is indicative of a larger phenomenon. Finally, it is shown that while inter-region interactions can be captured using daily data, intra-region interactions require more frequent samples.

Suggested Citation

  • Theo Diamandis & Yonathan Murin & Andrea Goldsmith, 2018. "Ranking Causal Influence of Financial Markets via Directed Information Graphs," Papers 1801.06896, arXiv.org.
    • Handle: RePEc:arx:papers:1801.06896
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1801.06896
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dimpfl Thomas & Peter Franziska Julia, 2013. "Using transfer entropy to measure information flows between financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 85-102, February.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Kwon, Okyu & Yang, Jae-Suk, 2008. "Information flow between composite stock index and individual stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2851-2856.
    4. Dimpfl, Thomas & Peter, Franziska J., 2014. "The impact of the financial crisis on transatlantic information flows: An intraday analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 1-13.
    5. John Quiggin, 2012. "Zombie Economics: How Dead Ideas Still Walk among Us: With a new chapter by the author," Economics Books, Princeton University Press, edition 1, number 9702.
    6. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    7. Petr Jizba & Hagen Kleinert & Mohammad Shefaat, 2011. "Renyi's information transfer between financial time series," Papers 1106.5913, arXiv.org, revised Jan 2012.
    8. Jizba, Petr & Kleinert, Hagen & Shefaat, Mohammad, 2012. "Rényi’s information transfer between financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(10), pages 2971-2989.
    9. Ratanapakorn, Orawan & Sharma, Subhash C., 2002. "Interrelationships among regional stock indices," Review of Financial Economics, Elsevier, vol. 11(2), pages 91-108.
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    11. Yonathan Murin & Jeremy Kim & Josef Parvizi & Andrea Goldsmith, 2018. "SozRank: A new approach for localizing the epileptic seizure onset zone," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-26, January.
    12. Leonidas Sandoval Junior & Asher Mullokandov & Dror Y. Kenett, 2015. "Dependency Relations among International Stock Market Indices," JRFM, MDPI, vol. 8(2), pages 1-39, May.
    13. Okyu Kwon & Jae-Suk Yang, 2008. "Information flow between stock indices," Papers 0802.1747, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    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. Nie, Chun-Xiao, 2023. "Time-varying characteristics of information flow networks in the Chinese market: An analysis based on sector indices," Finance Research Letters, Elsevier, vol. 54(C).
    2. Leonidas Sandoval Junior & Asher Mullokandov & Dror Y. Kenett, 2015. "Dependency Relations among International Stock Market Indices," JRFM, MDPI, vol. 8(2), pages 1-39, May.
    3. Xie, Wen-Jie & Yong, Yang & Wei, Na & Yue, Peng & Zhou, Wei-Xing, 2021. "Identifying states of global financial market based on information flow network motifs," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    5. Dimpfl, Thomas & Peter, Franziska J., 2014. "The impact of the financial crisis on transatlantic information flows: An intraday analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 1-13.
    6. Lu, Jingen & Chen, Xiaohong & Liu, Xiaoxing, 2018. "Stock market information flow: Explanations from market status and information-related behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 837-848.
    7. Ferreira, Paulo & Almeida, Dora & Dionísio, Andreia & Bouri, Elie & Quintino, Derick, 2022. "Energy markets – Who are the influencers?," Energy, Elsevier, vol. 239(PA).
    8. Leonidas Sandoval Junior, 2014. "Dynamics in two networks based on stocks of the US stock market," Papers 1408.1728, arXiv.org, revised Aug 2014.
    9. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.
    10. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    11. Dima, Bogdan & Dima, Ştefana Maria & Barna, Flavia, 2014. "The signaling effect of tax rates under fiscal competition: A (Shannonian) transfer entropy approach," Economic Modelling, Elsevier, vol. 42(C), pages 373-381.
    12. Xiao, Di & Wang, Jun, 2020. "Dynamic complexity and causality of crude oil and major stock markets," Energy, Elsevier, vol. 193(C).
    13. Dimpfl, Thomas & Peter, Franziska J., 2018. "Analyzing volatility transmission using group transfer entropy," Energy Economics, Elsevier, vol. 75(C), pages 368-376.
    14. Huynh, Toan Luu Duc & Nasir, Muhammad Ali & Vo, Xuan Vinh & Nguyen, Thong Trung, 2020. "“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    15. Parthajit Kayal & Moinak Maiti, 2023. "Examining the asymmetric information flow between pairs of gold, silver, and oil: a transfer entropy approach," SN Business & Economics, Springer, vol. 3(10), pages 1-22, October.
    16. Assaf, Ata & Bilgin, Mehmet Huseyin & Demir, Ender, 2022. "Using transfer entropy to measure information flows between cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    17. Peng Yue & Qing Cai & Wanfeng Yan & Wei-Xing Zhou, 2020. "Information flow networks of Chinese stock market sectors," Papers 2004.08759, arXiv.org.
    18. Christophe Chorro & Emmanuelle Jay & Philippe De Peretti & Thibault Soler, 2021. "Frequency causality measures and Vector AutoRegressive (VAR) models: An improved subset selection method suited to parsimonious systems," Documents de travail du Centre d'Economie de la Sorbonne 21013, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    19. Storhas, Dominik P. & De Mello, Lurion & Singh, Abhay Kumar, 2020. "Multiscale lead-lag relationships in oil and refined product return dynamics: A symbolic wavelet transfer entropy approach," Energy Economics, Elsevier, vol. 92(C).
    20. Neto, David, 2021. "Are Google searches making the Bitcoin market run amok? A tail event analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).

    More about this item

    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:arx:papers:1801.06896. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.