Contagion in Financial Networks

Author(s):  
Prasanna Gai ◽  
Sujit Kapadia
Keyword(s):  
2009 ◽  
Author(s):  
Gwenaël Moysan ◽  
Camille Cornand
Keyword(s):  

2021 ◽  
pp. 0308518X2110296
Author(s):  
Jonathan Beaverstock ◽  
Adam Leaver ◽  
Daniel Tischer

During the 2010s, collateralized loan obligations rapidly became a trillion-dollar industry, mirroring the growth profile and peak value of its cousin—collateralized debt obligations—in the 2000s. Yet, despite similarities in product form and growth trajectory, surprisingly little is known about how these markets evolved spatially and relationally. This paper fills that knowledge gap by asking two questions: how did each network adapt to achieve scale at speed across different jurisdictions; and to what extent does the spatial and relational organization of today's collateralized loan obligation structuration network, mirror that of collateralized debt obligations pre-crisis? To answer those questions, we draw on the global financial networks approach, developing our own concept of the networked product to explore the agentic qualities of collateralized debt obligations and collateralized loan obligations—specifically how their technical and regulatory “needs” shape the roles and jurisdictions enrolled in a global financial network. We use social network analysis to map and analyze the evolving spatial and relational organization that nurtured this growth, drawing on data harvested from offering circulars. We find that collateralized debt obligations spread from the US to Europe through a process of transduplication—that similar role-based network relations were reproduced from one regulatory regime to another. We also find a strong correlation between pre-crisis collateralized debt obligation- and post-crisis collateralized loan obligation-global financial networks in both US$- and €-denominations, with often the same network participants involved in each. We conclude by reflecting on the prosaic way financial markets for ostensibly complex products reproduce and the capacity for network stabilities to produce market instabilities.


2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Stefano Battiston ◽  
Michelangelo Puliga ◽  
Rahul Kaushik ◽  
Paolo Tasca ◽  
Guido Caldarelli

2018 ◽  
Vol 8 (3) ◽  
pp. 276-280 ◽  
Author(s):  
Simon XB Zhao

The state plays an important role in the development of financial networks at home and abroad, and the Chinese state is no exception. However, the influence of the state in global financial networks (GFNs) is anything but clear. This commentary offers a response to Töpfer’s theorization of China’s role in state-led GFNs by considering the case of Alibaba in order to address a highly abstract theory. In doing so, I question whether any theory of GFNs that places the state at its center represents a useful, if preliminary, guide to international financial networks, especially when dealing with China.


2018 ◽  
Vol 5 (8) ◽  
pp. 180577 ◽  
Author(s):  
Ana Sofia Ribeiro ◽  
Flávio L. Pinheiro ◽  
Francisco C. Santos ◽  
Amélia Polónia ◽  
Jorge M. Pacheco

Little is known about the structural patterns and dynamics of the first global trading market (FGTM), which emerged during the sixteenth century as a result of the Iberian expansion, let alone how it compares to today's global financial markets. Here we build a representative network of the FGTM using information contained in 8725 (handwritten) Bills of Exchange from that time—which were (human) interpreted and digitalized into an online database. We show that the resulting temporal network exhibits a hierarchical, highly clustered and disassortative structure, with a power-law dependence on the connectivity that remains remarkably robust throughout the entire period investigated. Temporal analysis shows that, despite major turnovers in the number and nature of the links—suggesting fast adaptation in response to the geopolitical and financial turmoil experienced at the time—the overall characteristics of the FGTM remain robust and virtually unchanged. The methodology developed here demonstrates the possibility of building and analysing complex trading and finance networks originating from pre-statistical eras, enabling us to highlight the striking similarities between the structural patterns of financial networks separated by centuries in time.


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