16,890 research outputs found
Systemic risk analysis in reconstructed economic and financial networks
We address a fundamental problem that is systematically encountered when
modeling complex systems: the limitedness of the information available. In the
case of economic and financial networks, privacy issues severely limit the
information that can be accessed and, as a consequence, the possibility of
correctly estimating the resilience of these systems to events such as
financial shocks, crises and cascade failures. Here we present an innovative
method to reconstruct the structure of such partially-accessible systems, based
on the knowledge of intrinsic node-specific properties and of the number of
connections of only a limited subset of nodes. This information is used to
calibrate an inference procedure based on fundamental concepts derived from
statistical physics, which allows to generate ensembles of directed weighted
networks intended to represent the real system, so that the real network
properties can be estimated with their average values within the ensemble. Here
we test the method both on synthetic and empirical networks, focusing on the
properties that are commonly used to measure systemic risk. Indeed, the method
shows a remarkable robustness with respect to the limitedness of the
information available, thus representing a valuable tool for gaining insights
on privacy-protected economic and financial systems
Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction
Assessing systemic risk in financial markets is of great importance but it
often requires data that are unavailable or available at a very low frequency.
For this reason, systemic risk assessment with partial information is
potentially very useful for regulators and other stakeholders. In this paper we
consider systemic risk due to fire sales spillover and portfolio rebalancing by
using the risk metrics defined by Greenwood et al. (2015). By using the Maximum
Entropy principle we propose a method to assess aggregated and single bank's
systemicness and vulnerability and to statistically test for a change in these
variables when only the information on the size of each bank and the
capitalization of the investment assets are available. We prove the
effectiveness of our method on 2001-2013 quarterly data of US banks for which
portfolio composition is available.Comment: 36 pages, 6 figures, Accepted on Journal of Economic Dynamics and
Contro
DebtRank: A microscopic foundation for shock propagation
The DebtRank algorithm has been increasingly investigated as a method to
estimate the impact of shocks in financial networks, as it overcomes the
limitations of the traditional default-cascade approaches. Here we formulate a
dynamical "microscopic" theory of instability for financial networks by
iterating balance sheet identities of individual banks and by assuming a simple
rule for the transfer of shocks from borrowers to lenders. By doing so, we
generalise the DebtRank formulation, both providing an interpretation of the
effective dynamics in terms of basic accounting principles and preventing the
underestimation of losses on certain network topologies. Depending on the
structure of the interbank leverage matrix the dynamics is either stable, in
which case the asymptotic state can be computed analytically, or unstable,
meaning that at least one bank will default. We apply this framework to a
dataset of the top listed European banks in the period 2008 - 2013. We find
that network effects can generate an amplification of exogenous shocks of a
factor ranging between three (in normal periods) and six (during the crisis)
when we stress the system with a 0.5% shock on external (i.e. non-interbank)
assets for all banks.Comment: 10 pages, 2 figure
Network Sensitivity of Systemic Risk
A growing body of studies on systemic risk in financial markets has
emphasized the key importance of taking into consideration the complex
interconnections among financial institutions. Much effort has been put in
modeling the contagion dynamics of financial shocks, and to assess the
resilience of specific financial markets - either using real network data,
reconstruction techniques or simple toy networks. Here we address the more
general problem of how shock propagation dynamics depends on the topological
details of the underlying network. To this end we consider different realistic
network topologies, all consistent with balance sheets information obtained
from real data on financial institutions. In particular, we consider networks
of varying density and with different block structures, and diversify as well
in the details of the shock propagation dynamics. We confirm that the systemic
risk properties of a financial network are extremely sensitive to its network
features. Our results can aid in the design of regulatory policies to improve
the robustness of financial markets
Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis
Interbank markets are often characterised in terms of a core-periphery
network structure, with a highly interconnected core of banks holding the
market together, and a periphery of banks connected mostly to the core but not
internally. This paradigm has recently been challenged for short time scales,
where interbank markets seem better characterised by a bipartite structure with
more core-periphery connections than inside the core. Using a novel
core-periphery detection method on the eMID interbank market, we enrich this
picture by showing that the network is actually characterised by multiple
core-periphery pairs. Moreover, a transition from core-periphery to bipartite
structures occurs by shortening the temporal scale of data aggregation. We
further show how the global financial crisis transformed the market, in terms
of composition, multiplicity and internal organisation of core-periphery pairs.
By unveiling such a fine-grained organisation and transformation of the
interbank market, our method can find important applications in the
understanding of how distress can propagate over financial networks.Comment: 17 pages, 9 figures, 1 tabl
Enhanced reconstruction of weighted networks from strengths and degrees
Network topology plays a key role in many phenomena, from the spreading of
diseases to that of financial crises. Whenever the whole structure of a network
is unknown, one must resort to reconstruction methods that identify the least
biased ensemble of networks consistent with the partial information available.
A challenging case, frequently encountered due to privacy issues in the
analysis of interbank flows and Big Data, is when there is only local
(node-specific) aggregate information available. For binary networks, the
relevant ensemble is one where the degree (number of links) of each node is
constrained to its observed value. However, for weighted networks the problem
is much more complicated. While the naive approach prescribes to constrain the
strengths (total link weights) of all nodes, recent counter-intuitive results
suggest that in weighted networks the degrees are often more informative than
the strengths. This implies that the reconstruction of weighted networks would
be significantly enhanced by the specification of both strengths and degrees, a
computationally hard and bias-prone procedure. Here we solve this problem by
introducing an analytical and unbiased maximum-entropy method that works in the
shortest possible time and does not require the explicit generation of
reconstructed samples. We consider several real-world examples and show that,
while the strengths alone give poor results, the additional knowledge of the
degrees yields accurately reconstructed networks. Information-theoretic
criteria rigorously confirm that the degree sequence, as soon as it is
non-trivial, is irreducible to the strength sequence. Our results have strong
implications for the analysis of motifs and communities and whenever the
reconstructed ensemble is required as a null model to detect higher-order
patterns
Network-based indicators of Bitcoin bubbles
The functioning of the cryptocurrency Bitcoin relies on the open availability
of the entire history of its transactions. This makes it a particularly
interesting socio-economic system to analyse from the point of view of network
science. Here we analyse the evolution of the network of Bitcoin transactions
between users. We achieve this by using the complete transaction history from
December 5th 2011 to December 23rd 2013. This period includes three bubbles
experienced by the Bitcoin price. In particular, we focus on the global and
local structural properties of the user network and their variation in relation
to the different period of price surge and decline. By analysing the temporal
variation of the heterogeneity of the connectivity patterns we gain insights on
the different mechanisms that take place during bubbles, and find that hubs
(i.e., the most connected nodes) had a fundamental role in triggering the burst
of the second bubble. Finally, we examine the local topological structures of
interactions between users, we discover that the relative frequency of triadic
interactions experiences a strong change before, during and after a bubble, and
suggest that the importance of the hubs grows during the bubble. These results
provide further evidence that the behaviour of the hubs during bubbles
significantly increases the systemic risk of the Bitcoin network, and discuss
the implications on public policy interventions
Epidemics of Liquidity Shortages in Interbank Markets
Financial contagion from liquidity shocks has being recently ascribed as a
prominent driver of systemic risk in interbank lending markets. Building on
standard compartment models used in epidemics, in this work we develop an EDB
(Exposed-Distressed-Bankrupted) model for the dynamics of liquidity shocks
reverberation between banks, and validate it on electronic market for interbank
deposits data. We show that the interbank network was highly susceptible to
liquidity contagion at the beginning of the 2007/2008 global financial crisis,
and that the subsequent micro-prudential and liquidity hoarding policies
adopted by banks increased the network resilience to systemic risk---yet with
the undesired side effect of drying out liquidity from the market. We finally
show that the individual riskiness of a bank is better captured by its network
centrality than by its participation to the market, along with the currently
debated concept of "too interconnected to fail"
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
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