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Financial big data analysis for the estimation of systemic risks

Author

Listed:
  • Paola Cerchiello

    (Department of Economics and Management, University of Pavia)

  • Paolo Giudici

    (Department of Economics and Management, University of Pavia)

Abstract

Systemic risk modelling concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them are more central and, therefore, more contagious/subject to contagion. The aim of this paper is to develop a novel systemic risk model. A model that, differently from existing ones, employs not only the information contained in financial market prices, but also big data coming from financial tweets. From a methodological viewpoint, the novelty of our paper is the estimation of systemic risk models using two different data sources: financial markets and financial tweets, and a proposal to combine them, using a Bayesian approach. From an applied viewpoint, we present the first systemic risk model based on big data, and show that such a model can shed further light on the interrelationships between financial institutions.

Suggested Citation

  • Paola Cerchiello & Paolo Giudici, 2014. "Financial big data analysis for the estimation of systemic risks," DEM Working Papers Series 086, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:086
    as

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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0086.pdf
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    References listed on IDEAS

    as
    1. Brunnermeier, Markus K. & Oehmke, Martin, 2013. "Bubbles, Financial Crises, and Systemic Risk," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1221-1288, Elsevier.
    2. Idier, Julien & Lamé, Gildas & Mésonnier, Jean-Stéphane, 2014. "How useful is the Marginal Expected Shortfall for the measurement of systemic exposure? A practical assessment," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 134-146.
    3. Jan Beirlant & John H. J. Einmahl, 2010. "Asymptotics for the Hirsch Index," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 355-364, September.
    4. Paola Cerchiello & Paolo Giudici, 2014. "How to measure the quality of financial tweets," DEM Working Papers Series 069, University of Pavia, Department of Economics and Management.
    5. Luca Pratelli & Alberto Baccini & Lucio Barabesi & Marzia Marcheselli, 2012. "Statistical Analysis of the Hirsch Index," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(4), pages 681-694, December.
    6. Wolfgang Glänzel, 2006. "On the h-index - A mathematical approach to a new measure of publication activity and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 67(2), pages 315-321, May.
    7. repec:pri:metric:wp047_2012_brunnermeier_ssrn-id2103814.pdf is not listed on IDEAS
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    More about this item

    Keywords

    Twitter data analysis; Graphical Gaussian models; Graphical Model selection; Banking and Finance applications; Risk Management;
    All these keywords.

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