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Complex Financial Networks and Systemic Risk: A Review

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Complexity and Geographical Economics

Part of the book series: Dynamic Modeling and Econometrics in Economics and Finance ((DMEF,volume 19))

Abstract

In this paper we review recent advances in financial economics in relation to the measurement of systemic risk. We start by reviewing studies that apply traditional measures of risk to financial institutions. However, the main focus of the review is on studies that use network analysis paying special attention to those that apply complex analysis techniques. Applications of these techniques for the analysis and pricing of systemic risk has already provided significant benefits at least at the conceptual level but it also looks very promising from a practical point of view.

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Notes

  1. 1.

    See Brunnermeier and Oehmke (2012) for a review of this literature.

  2. 2.

    For an interesting exposition of the geographic distribution of bank failures during the recent global financial crisis see Aubuchon and Wheelock (2010).

  3. 3.

    For expositions of network theory and its applications to economics see Goyal (2009), Jackson (2008) and Vega-Redondo (2007).

  4. 4.

    See Allen and Babus (2009) for a review of various applications of network theory to the study of financial issues.

  5. 5.

    See, for example, Haldane and May (2011), May et al. (2008). For a similar perspective on the benefits of network analysis, see also Schweitzer et al. (2009).

  6. 6.

    There is an established literature, known as econophysics, that has used complex systems to analyze various economic systems including the behavior of asset prices (for a review see Varela et al. 2015, this volume).

  7. 7.

    For a review of systemic risk measures applied to general financial systems see Markellof et al. (2012). See also European Central Bank (2007) for some theoretical and empirical contributions related to the measurement of systemic risk in banking and non-banking financial systems.

  8. 8.

    A put option is a derivative that offers the right, but not the obligation, to the holder to sell the underlying asset at a pre-specified price within a given period.

  9. 9.

    For an example of CCA see Lehar (2005) who applies the methodology to a sample of international banks from 1988 until 2002.

  10. 10.

    A CDS is a financial swap agreement whereby the seller of the contract will compensate the buyer in the event of a loan default. The buyer of the CDS makes a series of payments to the seller but only receives a payoff if the loan defaults.

  11. 11.

    The second indicator, known as Tail-β, has been also applied to the European banking system by De Jonghe (2010).

  12. 12.

    Applications of this methodology include Wong and Fong (2010) who provide CoVaR estimates for the CDS of Asia-Pacific Banks and Gauthier et al. (2009) who give systemic risk estimates for the Canadian banking system.

  13. 13.

    For a more thorough exposition of network theory see Varela et al. (2015) in this volume.

  14. 14.

    Other researchers use complex analysis for understanding interfirm financial (trade-credit chains) networks; see Battiston et al. (2007), Cossin and Schellhorn (2007), Kiyotaki and Moore (2004) and Lagunoff and Schreft (2001).

  15. 15.

    The two-bank version of the Diamond and Dybvig (1983) is analyzed by Bhattacharya and Gale (1987), however, a minimum of three banks are needed for comparisons of network structures to become meaningful.

  16. 16.

    Random networks correspond to the classical random graphs structures introduced by Erdös and Rényi (1959).

  17. 17.

    For traditional finance models addressing these issues, see Acharya et al. (2011) and Diamond and Rajan (2011).

  18. 18.

    Extreme conditions within a financial network should be understood as a systemic event, like the recent global financial crisis.

  19. 19.

    See Castiliognesi and Navarro (2007) for a similar result.

  20. 20.

    For a detailed description of the methodology used to perform these simulations see Soramäki et al. (2007a,b). There are some similarities between some of these simulation exercises and the counterfactual methodology reviewed above.

  21. 21.

    For more on this distinction, see Saunders et al. (2009).

  22. 22.

    When cross-border risk exposures are significant, as, for example, during the recent global financial crisis, it is also important to ensure there is international coordination among financial regulators; see Acharya et al. (2009b).

  23. 23.

    Data limitations are a serious constraining factor both for researchers that aim to measure systemic risk and practioners who are interested in controlling it; see Cerutti et al. (2012).

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Acknowledgements

We would like to acknowledge financial support from COST Action IS1104 “The EU in the new economic complex geography: models, tools and policy analysis”.

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  1. School of Economics, University of Nottingham, Nottingham, NG7 2RD, UK

    Spiros Bougheas

  2. Faculte de Droit et de Sciences Politiques, Aix-Marseille Université, Directeur d’études à l’EHESS, Membre de l’IUF, GREQAM, Bureau 109, 3 avenue Robert-Schuman, 13628, Aix-en-Provence, France

    Alan Kirman

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    Saime Kayam

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    Ingrid Kubin

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Bougheas, S., Kirman, A. (2015). Complex Financial Networks and Systemic Risk: A Review. In: Commendatore, P., Kayam, S., Kubin, I. (eds) Complexity and Geographical Economics. Dynamic Modeling and Econometrics in Economics and Finance, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-12805-4_6

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