19,379 research outputs found
Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation
As economic entities become increasingly interconnected, a shock in a
financial network can provoke significant cascading failures throughout the
system. To study the systemic risk of financial systems, we create a bi-partite
banking network model composed of banks and bank assets and propose a cascading
failure model to describe the risk propagation process during crises. We
empirically test the model with 2007 US commercial banks balance sheet data and
compare the model prediction of the failed banks with the real failed banks
after 2007. We find that our model efficiently identifies a significant portion
of the actual failed banks reported by Federal Deposit Insurance Corporation.
The results suggest that this model could be useful for systemic risk stress
testing for financial systems. The model also identifies that commercial rather
than residential real estate assets are major culprits for the failure of over
350 US commercial banks during 2008-2011.Comment: 13 pages, 7 figure
The multi-layer network nature of systemic risk and its implications for the costs of financial crises
The inability to see and quantify systemic financial risk comes at an immense
social cost. Systemic risk in the financial system arises to a large extent as
a consequence of the interconnectedness of its institutions, which are linked
through networks of different types of financial contracts, such as credit,
derivatives, foreign exchange and securities. The interplay of the various
exposure networks can be represented as layers in a financial multi-layer
network. In this work we quantify the daily contributions to systemic risk from
four layers of the Mexican banking system from 2007-2013. We show that focusing
on a single layer underestimates the total systemic risk by up to 90%. By
assigning systemic risk levels to individual banks we study the systemic risk
profile of the Mexican banking system on all market layers. This profile can be
used to quantify systemic risk on a national level in terms of nation-wide
expected systemic losses. We show that market-based systemic risk indicators
systematically underestimate expected systemic losses. We find that expected
systemic losses are up to a factor four higher now than before the financial
crisis of 2007-2008. We find that systemic risk contributions of individual
transactions can be up to a factor of thousand higher than the corresponding
credit risk, which creates huge risks for the public. We find an intriguing
non-linear effect whereby the sum of systemic risk of all layers underestimates
the total risk. The method presented here is the first objective data driven
quantification of systemic risk on national scales that reveal its true levels.Comment: 15 pages, 6 figure
Clearing algorithms and network centrality
I show that the solution of a standard clearing model commonly used in
contagion analyses for financial systems can be expressed as a specific form of
a generalized Katz centrality measure under conditions that correspond to a
system-wide shock. This result provides a formal explanation for earlier
empirical results which showed that Katz-type centrality measures are closely
related to contagiousness. It also allows assessing the assumptions that one is
making when using such centrality measures as systemic risk indicators. I
conclude that these assumptions should be considered too strong and that, from
a theoretical perspective, clearing models should be given preference over
centrality measures in systemic risk analyses
Derivatives and Credit Contagion in Interconnected Networks
The importance of adequately modeling credit risk has once again been
highlighted in the recent financial crisis. Defaults tend to cluster around
times of economic stress due to poor macro-economic conditions, {\em but also}
by directly triggering each other through contagion. Although credit default
swaps have radically altered the dynamics of contagion for more than a decade,
models quantifying their impact on systemic risk are still missing. Here, we
examine contagion through credit default swaps in a stylized economic network
of corporates and financial institutions. We analyse such a system using a
stochastic setting, which allows us to exploit limit theorems to exactly solve
the contagion dynamics for the entire system. Our analysis shows that, by
creating additional contagion channels, CDS can actually lead to greater
instability of the entire network in times of economic stress. This is
particularly pronounced when CDS are used by banks to expand their loan books
(arguing that CDS would offload the additional risks from their balance
sheets). Thus, even with complete hedging through CDS, a significant loan book
expansion can lead to considerably enhanced probabilities for the occurrence of
very large losses and very high default rates in the system. Our approach adds
a new dimension to research on credit contagion, and could feed into a rational
underpinning of an improved regulatory framework for credit derivatives.Comment: 26 pages, 7 multi-part figure
- âŠ