Systemic Risk in Networks

Nils Detering (), Thilo Meyer-Brandis (), Konstantinos Panagiotou () and Daniel Ritter ()
Additional contact information
Nils Detering: University of California, Department of Statistics and Applied Probability
Thilo Meyer-Brandis: University of Munich, Department of Mathematics
Konstantinos Panagiotou: University of Munich, Department of Mathematics
Daniel Ritter: University of Munich, Department of Mathematics

Chapter Chapter 5 in Network Science, 2019, pp 59-77 from Springer

Abstract: Abstract Systemic risk, i.e., the risk that a local shock propagates throughout a given system due to contagion effects, is of great importance in many fields of our lives. In this summary article, we show how asymptotic methods for random graphs can be used to understand and quantify systemic risk in networks. We define a notion of resilient networks and present criteria that allow us to classify networks as resilient or non-resilient. We further examine the question how networks can be strengthened to ensure resilience. In particular, for financial systems we address the question of sufficient capital requirements. We present the results in random graph models of increasing complexity and relate them to classical results about the phase transition in the Erdös-Rényi model. We illustrate the results by a small simulation study.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-26814-5_5

Ordering information: This item can be ordered from
http://www.springer.com/9783030268145

DOI: 10.1007/978-3-030-26814-5_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().