A Bayesian Methodology for Systemic Risk Assessment in Financial Networks

Gandy, Axel; Veraart, Luitgard A. M.

doi:10.1287/mnsc.2016.2546

Cited by 131 publications

(119 citation statements)

References 47 publications

(28 reference statements)

Supporting

Mentioning

110

Contrasting

Order By: Relevance

“…The aforementioned approach has been recently extended to account also for weights, through an algorithm which is not dissimilar in spirit from the DECM. More specifically, the model introduced in [116] is described by the following probability distribution for link weights:…”

Section: A Bayesian Approach To Network Reconstructionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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...

show abstract

Section: A Bayesian Approach To Network Reconstructionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…We use the same dataset from 2011 of European banks from the European Banking Authority that has been used in previous studies relying on the Eisenberg-Noe framework (Gandy and Veraart (2016), Chen et al (2016)). As in these papers, given the heuristic approach to the dataset, our exercise should be considered to be an illustration of our results and methodology, rather than a realistic full-fledged empirical analysis.…”

Section: Empirical Application: Assessing the Robustness Of Systemic mentioning

confidence: 99%

“…setting Greek bond values to zero. In our sensitivity analysis we resample the underlying liabilities matrix from the Gandy & Veraart algorithm Gandy and Veraart (2016) 1000 times.…”

Section: Empirical Application: Assessing the Robustness Of Systemic mentioning

confidence: 99%

Sensitivity of the Eisenberg-Noe Clearing Vector to Individual Interbank Liabilities

et al. 2017

View full text Add to dashboard Cite

We quantify the sensitivity of the Eisenberg-Noe clearing vector to estimation errors in the bilateral liabilities of a financial system. The interbank liabilities matrix is a crucial input to the computation of the clearing vector. However, in practice central bankers and regulators must often estimate this matrix because complete information on bilateral liabilities is rarely available. As a result, the clearing vector may suffer from estimation errors in the liabilities matrix. We quantify the clearing vector's sensitivity to such estimation errors and show that its directional derivatives are, like the clearing vector itself, solutions of fixed point equations. We describe estimation errors utilizing a basis for the space of matrices representing permissible perturbations and derive analytical solutions to the maximal deviations of the Eisenberg-Noe clearing vector. This allows us to compute upper bounds for the worst case perturbations of the clearing vector. Moreover, we quantify the probability of observing clearing vector deviations of a certain magnitude, for uniformly or normally distributed errors in the relative liability matrix.Applying our methodology to a dataset of European banks, we find that perturbations to the relative liabilities can result in economically sizeable differences that could lead to an underestimation of the risk of contagion. Importantly, our results allow regulators to bound the error of their simulations.

show abstract

“…All parameters and quantities introduced above are assumed to be known or have been estimated before. We refer to Anand et al (2015), Gandy and Veraart (2016) and Upper (2011) for various methods to estimate interfirm exposures. Based on the previously described set-up, the balance sheet of firm i at time t = 0 has the form depicted on the left-hand side of Figure 1.…”

Section: Default Risk Model For Interconnected Firmsmentioning

confidence: 99%

Contagion in Financial Systems: A Bayesian Network Approach

Chong

Klüppelberg

2016

SSRN Journal

View full text Add to dashboard Cite

We develop a structural default model for interconnected financial institutions in a probabilistic framework. For all possible network structures we characterize the joint default distribution of the system using Bayesian network methodologies. Particular emphasis is given to the treatment and consequences of cyclic financial linkages. We further demonstrate how Bayesian network theory can be applied to detect contagion channels within the financial network, to measure the systemic importance of selected entities on others, and to compute conditional or unconditional probabilities of default for single or multiple institutions.

show abstract

A Bayesian Methodology for Systemic Risk Assessment in Financial Networks

Cited by 131 publications

References 47 publications

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

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

Sensitivity of the Eisenberg-Noe Clearing Vector to Individual Interbank Liabilities

Contagion in Financial Systems: A Bayesian Network Approach

Contact Info

Product

Resources

About