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Quantifying Systemic Risk

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  • Haubrich, Joseph G.
  • Lo, Andrew W.

Abstract

In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.

Suggested Citation

  • Haubrich, Joseph G. & Lo, Andrew W. (ed.), 2013. "Quantifying Systemic Risk," National Bureau of Economic Research Books, University of Chicago Press, number 9780226319285, November.
    • Handle: RePEc:ucp:bknber:9780226319285
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    Cited by:

    1. Christophe Orazio & Rebeca Cordero Montoya & Margot Régolini & José G. Borges & Jordi Garcia-Gonzalo & Susana Barreiro & Brigite Botequim & Susete Marques & Róbert Sedmák & Róbert Smreček & Yvonne Bro, 2017. "Decision Support Tools and Strategies to Simulate Forest Landscape Evolutions Integrating Forest Owner Behaviour: A Review from the Case Studies of the European Project, INTEGRAL," Sustainability, MDPI, vol. 9(4), pages 1-31, April.
    2. Matteo Serri & Guido Caldarelli & Giulio Cimini, 2016. "How the interbank market becomes systemically dangerous: an agent-based network model of financial distress propagation," Papers 1611.04311, arXiv.org.
    3. in ’t Veld, Daan & van Lelyveld, Iman, 2014. "Finding the core: Network structure in interbank markets," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 27-40.
    4. Lu Bai & Lixin Cui & Lixiang Xu & Yue Wang & Zhihong Zhang & Edwin R. Hancock, 2019. "Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis," Papers 1910.09153, arXiv.org.
    5. Anufriev, Mikhail & Panchenko, Valentyn, 2015. "Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 241-255.

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