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Systemic Risk and Default Clustering for Large Financial Systems

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  • Konstantinos Spiliopoulos

Abstract

As it is known in the finance risk and macroeconomics literature, risk-sharing in large portfolios may increase the probability of creation of default clusters and of systemic risk. We review recent developments on mathematical and computational tools for the quantification of such phenomena. Limiting analysis such as law of large numbers and central limit theorems allow to approximate the distribution in large systems and study quantities such as the loss distribution in large portfolios. Large deviations analysis allow us to study the tail of the loss distribution and to identify pathways to default clustering. Sensitivity analysis allows to understand the most likely ways in which different effects, such as contagion and systematic risks, combine to lead to large default rates. Such results could give useful insights into how to optimally safeguard against such events.

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  • Konstantinos Spiliopoulos, 2014. "Systemic Risk and Default Clustering for Large Financial Systems," Papers 1402.5352, arXiv.org, revised Feb 2015.
    • Handle: RePEc:arx:papers:1402.5352
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    Cited by:

    1. Konstantinos Spiliopoulos & Jia Yang, 2018. "Network effects in default clustering for large systems," Papers 1812.07645, arXiv.org, revised Feb 2020.
    2. Ben Hambly & Andreas Søjmark, 2019. "An SPDE model for systemic risk with endogenous contagion," Finance and Stochastics, Springer, vol. 23(3), pages 535-594, July.
    3. Fang Fei & Sun Yiwei & Spiliopoulos Konstantinos, 2017. "On the effect of heterogeneity on flocking behavior and systemic risk," Statistics & Risk Modeling, De Gruyter, vol. 34(3-4), pages 141-155, September.
    4. Fei Fang & Yiwei Sun & Konstantinos Spiliopoulos, 2016. "The effect of heterogeneity on flocking behavior and systemic risk," Papers 1607.08287, arXiv.org, revised Jun 2017.

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