Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction

Gangi, Domenico Di; Lillo, Fabrizio; Pirino, Davide

doi:10.1016/j.jedc.2018.07.001

Cited by 44 publications

(51 citation statements)

References 84 publications

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“…Therefore, it is not properly unexpected that several studies deal with systemic risk problems in the framework of complex networks (see e.g. [5], [13], [15], [23], [24] [32], [35]).…”

Section: Introductionmentioning

confidence: 99%

Systemic risk assessment through high order clustering coefficient

2020

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In this article we propose a novel measure of systemic risk in the context of financial networks.To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order l of a node i introduced in [10]. Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks.

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“…Therefore, it is not properly unexpected that several studies deal with systemic risk problems in the framework of complex networks (see e.g. [5], [13], [15], [23], [24] [32], [35]).…”

Section: Introductionmentioning

confidence: 99%

Systemic risk assessment through high order clustering coefficient

2020

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“…Yet, the MaxEnt prescription provides quite accurate estimates whenever only the magnitude of weights are considered [80,81]. The latter is the reason why MaxEnt is widely used in economics-the simple gravity model (without distance) has the same functional form of the MaxEnt estimate [82], and in finance-where it takes the same form of the capital asset pricing model (CAPM) [83,84]. As a final remark, we stress that the MaxEnt algorithm generates a unique reconstructed configuration, thus being classifiable as a deterministic algorithm.…”

Section: Reconstruction Methodsmentioning

confidence: 99%

“…An approach combining the MaxEnt and the ERG frameworks has been recently developed, under the name of Maximum Entropy CAPM (MECAPM) [84]. The idea is to maximize the Shannon entropy constraining not the expected values of the matrix marginals, but rather the expected value of each link weight.…”

Section: Combining Maxent and Erg Frameworkmentioning

confidence: 99%

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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“…Another centrality measure is the Systemicness of a bank introduced by Greenwood [45] which is proportional to the product of the size of a bank, its leverage, and connectedness. Information regarding these variables may not be readily obtainable, and Gangi [46] discusses how to obtain Systemicness based on only partial data about the network. A recent addition to the list of various centrality measures is the one proposed by Cont & Schaanning [47] which is based on liquidity weighted portfolio overlaps.…”

Section: Fire-walling Financial Networkmentioning

confidence: 99%

Systemic Risk: Fire-Walling Financial Systems Using Network-Based Approaches

Sasidevan¹,

Bertschinger²

2019

Network Theory and Agent-Based Modeling in Economics and Finance

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The latest financial crisis has painfully revealed the dangers arising from a globally interconnected financial system. Conventional approaches based on the notion of the existence of equilibrium and those which rely on statistical forecasting have seen to be inadequate to describe financial systems in any reasonable way. A more natural approach is to treat fi-nancial systems as complex networks of claims and obligations between various financial institutions present in an economy. The generic frame-work of complex networks has been successfully applied across several dis-ciplines, e.g., explaining cascading failures in power transmission systems and epidemic spreading. Here we review various network models address-ing financial contagion via direct inter-bank contracts and indirectly via overlapping portfolios of financial institutions. In particular, we discuss the implications of the "robust-yetfragile" nature of financial networks for cost-effective regulation of systemic risk.

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Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction

Cited by 44 publications

References 84 publications

Systemic risk assessment through high order clustering coefficient

Systemic risk assessment through high order clustering coefficient

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

Systemic Risk: Fire-Walling Financial Systems Using Network-Based Approaches

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