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Big data models of bank risk contagion

Author

Listed:
  • Paola Cerchiello

    (Department of Economics and Management, University of Pavia)

  • Paolo Giudici

    (Department of Economics and Management, University of Pavia)

  • Giancarlo Nicola

    (Department of Economics and Management, University of Pavia)

Abstract

A very important area of financial risk management is systemic risk modelling,which 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 systemic risk model which, 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, we propose a new framework, based on graphical models, that can estimate systemic risks with models based on two different sources: financial markets and financial tweets, and suggest a way 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. This can help predicting the level of returns of a bank, conditionally on the others, for example when a shock occurs in another bank, or exogeneously.

Suggested Citation

  • Paola Cerchiello & Paolo Giudici & Giancarlo Nicola, 2016. "Big data models of bank risk contagion," DEM Working Papers Series 117, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0117
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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0117.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. Sylvain Benoit & Jean-Edouard Colliard & Christophe Hurlin & Christophe Pérignon, 2017. "Where the Risks Lie: A Survey on Systemic Risk," Review of Finance, European Finance Association, vol. 21(1), pages 109-152.
    3. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
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    Cited by:

    1. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.
    2. Le, Richard & Ku, Hyejin, 2022. "Reducing systemic risk in a multi-layer network using reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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    More about this item

    Keywords

    Financial Risk Management; Graphical models; Systemic risks; Twitter data analysis;
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