Reconstructing a credit network

Caldarelli, Guido; Chessa, Alessandro; Pammolli, Fabio; Gabrielli, Andrea; Puliga, Michelangelo

doi:10.1038/nphys2580

Cited by 74 publications

(77 citation statements)

References 8 publications

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“…More refined methods such as sparse reconstruction algorithms [2] allow one to obtain a matrix with an arbitrary level of heterogeneity, however, without prescribing how to identify its proper value; moreover, even when the link density is correctly recovered, systemic risk is again underestimated because of the homogeneity principle used to obtain the link * giulio.cimini@roma1.infn.it weights. A more recent approach [9,10] instead uses the limited topological information available on the network to generate an ensemble of graphs according to the configuration model (CM) [11], where the Lagrange multipliers that define it are replaced by fitnesses [12], i.e., node-specific properties assumed to be known-in a way similar to fitness-dependent network models [13]. The estimation of network properties is then carried out within such an ensemble.…”

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Estimating topological properties of weighted networks from limited information

et al. 2015

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A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems. Reconstructing the statistical properties of a network when only partial information is available represents a key open problem in the field of statistical physics of complex systems [1,2]. Yet, addressing this issue can lead to many concrete applications. A paramount example is provided by financial networks, where nodes represent financial institutions and links stand for the various types of financial ties, such as loans or derivative contracts. These ties result in dependencies among institutions and constitute the ground for the propagation of financial distress across the network. However, due to confidentiality issues, the information that regulators are able to collect on mutual exposures is very limited [3], hindering the analysis of the system resilience to the spreading of financial distress-which depends on the structure of the whole network [4,5]. Typically, the analysis of systemic risk has been pursued by trying to estimate the unknown link weights of the network via a maximum homogeneity principle [6][7][8], looking for the adjacency matrix with minimal distance from the uniform matrix that also satisfies the imposed constraints (e.g., the budget of individual banks). These approaches are also known as dense reconstruction methods, as they assume that the network is fully connected, a hypothesis that represents their strongest limitation. In fact, not only empirical networks do show a very heterogeneous distribution of the connectivity, but such a dense reconstruction leads to systemic risk underestimation [2,8]. More refined methods such as sparse reconst...

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Estimating topological properties of weighted networks from limited information

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“…Such a modeling proved to be fruitful in the description of a variety of different phenomena ranging from biology [2] to social sciences [3]–[6]. Here we move forward by considering the evolution of the community structure of a particular instance of complex networks.…”

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The Rise of China in the International Trade Network: A Community Core Detection Approach

et al. 2014

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Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995–2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism.

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“…the ratio of the links present over the possible ones) as they are built from cross-correlation of pairs of financial time series. Such correlation levels are most of the time statistically significant17181920212223. At the onset of the crisis the networks become even more dense, have stronger links, and their minimum spanning trees display shorter average distance.…”

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Credit Default Swaps networks and systemic risk

Puliga

Caldarelli

Battiston

2014

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Credit Default Swaps (CDS) spreads should reflect default risk of the underlying corporate debt. Actually, it has been recognized that CDS spread time series did not anticipate but only followed the increasing risk of default before the financial crisis. In principle, the network of correlations among CDS spread time series could at least display some form of structural change to be used as an early warning of systemic risk. Here we study a set of 176 CDS time series of financial institutions from 2002 to 2011. Networks are constructed in various ways, some of which display structural change at the onset of the credit crisis of 2008, but never before. By taking these networks as a proxy of interdependencies among financial institutions, we run stress-test based on Group DebtRank. Systemic risk before 2008 increases only when incorporating a macroeconomic indicator reflecting the potential losses of financial assets associated with house prices in the US. This approach indicates a promising way to detect systemic instabilities.

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Reconstructing a credit network

Cited by 74 publications

References 8 publications

Estimating topological properties of weighted networks from limited information

Estimating topological properties of weighted networks from limited information

The Rise of China in the International Trade Network: A Community Core Detection Approach

Credit Default Swaps networks and systemic risk

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