Systemic risk, here meant as the risk of default of a large portion of the financial system, depends on the network of financial exposures among institutions. However, there is no widely accepted methodology to determine the systemically important nodes in a network. To fill this gap, we introduce, DebtRank, a novel measure of systemic impact inspired by feedback-centrality. As an application, we analyse a new and unique dataset on the USD 1.2 trillion FED emergency loans program to global financial institutions during 2008–2010. We find that a group of 22 institutions, which received most of the funds, form a strongly connected graph where each of the nodes becomes systemically important at the peak of the crisis. Moreover, a systemic default could have been triggered even by small dispersed shocks. The results suggest that the debate on too-big-to-fail institutions should include the even more serious issue of too-central-to-fail.

/pdf/debtrank-too-central-to-fail-financial-networks-the-fed-and-4v2qibr1w5.pdf

DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers.

/pdf/the-network-of-global-corporate-control-3aivpismcl.pdf

The network of global corporate control

Cours d'economie politique

We characterize the evolution over time of a network of credit relations among financial agents as a system of coupled stochastic processes. Each process describes the dynamics of individual financial robustness, while the coupling results from a network of liabilities among agents. The average level of risk diversification of the agents coincides with the density of links in the network. In addition to a process of diffusion of financial distress, we also consider a discrete process of default cascade, due to the re-evaluation of agents' assets. In this framework we investigate the probability of individual defaults as well as the probability of systemic default as a function of the network density. While it is usually thought that diversification of risk always leads to a more stable financial system, in our model a tension emerges between individual risk and systemic risk. As the number of counterparties in the credit network increases beyond a certain value, the default probability, both individual and systemic, starts to increase. This tension originates from the fact that agents are subject to a financial accelerator mechanism. In other words, individual financial fragility feeding back on itself may amplify the effect of an initial shock and lead to a full fledged systemic crisis. The results offer a simple possible explanation for the endogenous emergence of systemic risk in a credit network.

Liaisons Dangereuses: Increasing Connectivity, Risk Sharing, and Systemic Risk

https://academiccommons.columbia.edu/doi/10.7916/D8T15DJV/download

Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk

We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the industrial sector plays only a marginal role. We also analyze the meta-network in which the nodes are the communities and the links are obtained by aggregating the links among firms belonging to pairs of communities. We analyze the network centrality of the top 50 communities and we provide a quantitative assessment of the financial sector role in connecting the global economy.

/pdf/the-community-structure-of-the-global-corporate-network-cpv80u83tc.pdf

The community structure of the global corporate network.

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.

/pdf/multi-scale-analysis-of-the-european-airspace-using-network-47v22j2esv.pdf

Multi-scale analysis of the European airspace using network community detection

In this paper, we investigate the network of ownership relationships among European firms and its embedding in the geographical space. We carry out a detailed analysis of geographical distances between pairs of nodes, connected by edges or by shortest paths of varying length. In particular, we study the relation between geographical distance and network distance in comparison with a random spatial network model. While the distribution of geographical distance can be fairly well reproduced, important deviations appear in the network distance and in the size of the largest strongly connected component. Our results show that geographical factors allow us to capture several features of the network, while the deviations quantify the effect of additional economic factors at work in shaping the topology. The analysis is relevant to other types of geographically embedded networks and sheds light on the link formation process in the presence of spatial constraints.

https://iopscience.iop.org/article/10.1088/1367-2630/13/6/063021/pdf

Geography versus topology in the European Ownership Network

In this article, we study innovation processes and technological change in an agent-based model. By including a behavioral switching among heterogeneous innovative firms, the model is able to replicate, via simulations, well-known industrial dynamic and growth type stylized facts. The main original element of the model is that firms are allowed to endogenously change among three different classes, namely, single innovators, collaborative innovators, and imitators. Moreover, our analysis focuses on the impact of these three innovation categories on micro, meso, and macro aggregates. We find that collaborative companies are those having the highest positive impact on the economic system. Furthermore, we have paid particular attention to the role of credit market in promoting smart growth. For this purpose, we analyze the role of banks as sources of external funds for innovative entrepreneurs. Our results suggest a trade-off between short-term profit maximization and long-term efficiency, which prevents banks to foster investment in R&D and technological progress. The model is then used to study the effect that different innovation policies have on macroeconomic performance.

/pdf/the-impact-of-classes-of-innovators-on-technology-financial-4l1zir305g.pdf

Stefania Vitali

Papers

The network of global corporate control

The community structure of the global corporate network.

Multi-scale analysis of the European airspace using network community detection

Geography versus topology in the European Ownership Network

The impact of classes of innovators on technology, financial fragility, and economic growth