The missing links: A global study on uncovering financial network structures from partial data

Anand, Kartik; Lelyveld, Iman van; Banai, Ádám; Friedrich, Soeren; Garratt, Rodney; Hałaj, Grzegorz; Fique, Jose; Hansen, Ib; Martínez-Jaramillo, Serafín; Lee, Hwayun; Molina-Borboa, José Luis; Nobili, Stefano; Rajan, Sriram; Salakhova, Dilyara; Silva, Thiago Christiano; Silvestri, Laura; Souza, S. R.

doi:10.1016/j.jfs.2017.05.012

Cited by 136 publications

(102 citation statements)

References 34 publications

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“…3) is different from the WCM by using these strengths not to directly reconstruct the network, but to estimate the degrees first, and only then to build the maximum entropy ensemble. In such a way it can generate sparse and non-trivial topological structures, and can faithfully be used to reconstruct complex networked systems [100,106].…”

Section: Box 2: Comparing Models From Different Constraintsmentioning

confidence: 99%

The statistical physics of real-world networks

Cimini¹,

Squartini²,

Saracco³

et al. 2019

Nat Rev Phys

311

308

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In the last 15 years, statistical physics has been a very successful framework to model complex networks. On the theoretical side, this approach has brought novel insights into a variety of physical phenomena, such as self-organisation, scale invariance, emergence of mixed distributions and ensemble non-equivalence, that display unconventional features on heterogeneous networks. At the same time, thanks to their deep connection with information theory, statistical physics and the principle of maximum entropy have led to the definition of null models for networks reproducing some features of real-world systems, but otherwise as random as possible. We review here the statistical physics approach and the various null models for complex networks, focusing in particular on the analytic frameworks reproducing the local network features. We then show how these models have been used to detect statistically significant and predictive structural patterns in real-world networks, as well as to reconstruct the network structure in case of incomplete information. We further survey the statistical physics models that reproduce more complex, semi-local network features using Markov chain Monte Carlo sampling, as well as the models of generalised network structures such as multiplex networks, interacting networks and simplicial complexes. arXiv:1810.05095v2 [physics.soc-ph]

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Section: Box 2: Comparing Models From Different Constraintsmentioning

confidence: 99%

The statistical physics of real-world networks

Cimini¹,

Squartini²,

Saracco³

et al. 2019

Nat Rev Phys

311

308

View full text Add to dashboard Cite

show abstract

“…Although the knowledge of the whole network structure could help regulators to take immediate countermeasures to stop the propagation of financial distress, this information is seldom available (the knowledge of the whole network of investments would pose immense problems of privacy), thus hindering the possibility of providing a realistic estimate of the extent of the contagion. As confirmed by the analysis of the various papers reported in this review, the incompleteness of network instances seems to be unavoidable [60,61]: since addressing the problem of estimating the resilience of financial networks cannot be addressed without knowing the structural details of national and cross-countries interbank networks, information theory seems indeed to provide the right framework to tackle this kind of problems.Finance is not the only domain affected by limitedness of information about nodes interdependencies: biological and ecological systems also exist (e.g., cell metabolic networks and ecological webs) whose interaction network is often only partially accessible due either to experimental limitations or observational constraints 2 .Approaching network reconstruction. In order to deal with the problem of missing information, many different approaches have been attempted so far.…”

mentioning

confidence: 78%

Reconstruction methods for networks: The case of economic and financial systems

et al. 2018

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The study of social, economic and biological systems is often (when not always) limited by the partial information about the structure of the underlying networks. An example of paramount importance is provided by financial systems: information on the interconnections between financial institutions is privacy-protected, dramatically reducing the possibility of correctly estimating crucial systemic properties such as the resilience to the propagation of shocks. The need to compensate for the scarcity of data, while optimally employing the available information, has led to the birth of a research field known as network reconstruction. Since the latter has benefited from the contribution of researchers working in disciplines as different as mathematics, physics and economics, the results achieved so far are still scattered across heterogeneous publications. Most importantly, a systematic comparison of the network reconstruction methods proposed up to now is currently missing. This review aims at providing a unifying framework to present all these studies, mainly focusing on their application to economic and financial networks. 7The problem of missing information. After an initial activity aimed at determining the structure of real-world networks by measuring standard topological quantities, a more theoretical activity was started, aiming at both defining new quantities and devising proper models to explain observations [45,44,52,53,54]. Given the complexity that can arise even from a simple mathematical model based upon graphs, researchers have recently focused on the development of a topological theory: loosely speaking, topological quantities are employed to define statistical models, rather than reproduced from microscopic dynamical rules [55,56,57,58,59].Unfortunately, when moving to the validation of such models a common problem arises: very often, the data available on the real network are either incomplete or imprecise (or both). This problem is particularly evident in the case of economic and financial networks: in this case, data collection suffers from the problem of partial accounting and the presence of disclosure requirements. In order to illustrate the importance of such an issue, let us think of a bipartite, financial network whose node sets represent investors and the investments they do. Although the knowledge of the whole network structure could help regulators to take immediate countermeasures to stop the propagation of financial distress, this information is seldom available (the knowledge of the whole network of investments would pose immense problems of privacy), thus hindering the possibility of providing a realistic estimate of the extent of the contagion. As confirmed by the analysis of the various papers reported in this review, the incompleteness of network instances seems to be unavoidable [60,61]: since addressing the problem of estimating the resilience of financial networks cannot be addressed without knowing the structural details of national and cross-countries interbank networks, inf...

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“…To overcome the problem, recently more sophisticated methods are also proposed [79]. Anand et al [80] compare the accuracy of several existing estimation methods by applying them to various empirical networks.…”

Section: Interbank Network In Different Countriesmentioning

confidence: 99%

Network models of financial systemic risk: a review

2017

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The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus, in particular, on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology.

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The missing links: A global study on uncovering financial network structures from partial data

Cited by 136 publications

References 34 publications

The statistical physics of real-world networks

The statistical physics of real-world networks

Reconstruction methods for networks: The case of economic and financial systems

Network models of financial systemic risk: a review

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