1,721,086 research outputs found
Geography versus topology in the European Ownership Network
Full text linkIn 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
Financial networks
No full textThe financial system performs vital functions for the world economy. Very often one of more aspect of this system can be described by means of a complex graph. In this chapter under the generic name of financial networks we indicate several different systems all related to the world of finance. © 2014 Springer International Publishing Switzerland
Statistical independence and neural computation in the leech ganglion
No full textIn this report, the input/output relations in an isolated ganglion of the leech Hirudo medicinalis were studied by simultaneously using six or eight suction pipettes and two intracellular electrodes. Sensory input was mimicked by eliciting action potentials in mechanosensory neurons with intracellular electrodes. The integrated neural output was measured by recording extracellular voltage signals with pipettes sucking the roots and the connectives. A single evoked action potential activated electrical activity in at least a dozen different neurons, some of which were identified. This electrical activity was characterized by a high degree of temporal and spatial variability. The action potentials of coactivated neurons, i.e. activated by the same mechanosensory neuron, did not show any significant pairwise correlation. Indeed, the analysis of evoked action potentials indicates clear statistical independence among coactivated neurons, presumably originating from the independence of synaptic transmission at distinct synapses. This statistical independence may be used to increase reliability when neuronal activity is averaged or pooled. It is suggested that statistical independence among coactivated neurons may be a usual property of distributed processing of neuronal networks and a basic feature of neural computation
Credit default swaps networks and systemic risk
Full text linkCredit 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
Sustainable investing and climate transition risk: A portfolio rebalancing approach
Full text linkThe authors studied how greenness can be combined with other investment criteria to construct sets of corporate bond portfolios with decreasing exposure to climate transition risk. They apply the methodology to the European Central Bank’s asset purchase program. They define a weaker market neutrality principle as investing proportionally to the bond amount outstanding within Climate Policy Relevant Sectors. The portfolio rebalancing leads to a 10% reduction of exposure to climate transition risk. Then, the authors studied the relationship between bonds’ rebalancing and issuers’ environmental, social, and governance (ESG) characteristics and greenhouse gas (GHG) emissions. Bonds issued by firms with low (high) ESG risk and GHG emissions are more likely to be bought (sold) in the rebalancing. Finally, they analyzed the implications of portfolio rebalancing on financial markets, finding that changes in yields would be limited to less than 80 basis points on individual bonds. The approach can contribute to inform climate-aware portfolio rebalancing and sustainable investment strategies
The topology of shareholding networks
No full textWe study the statistical properties of the network of shareholding relationships in the Italian stock market (MIB) and in two US stock markets (NYSE and NASDAQ). The networks are found to be scale free a feature which doesn't seem to be in accord with classical theories of portfolio optimization. Several statistical properties differ across markets and allow for a classification. We introduce two quantities, HI and SI, analogous to in-degree and out-degree for weighted graphs. The distribution of HI and SI allow us to characterize from a statistical perspective the individual ownership concentration of stocks and the individual power of holders. They also suggest two different global pictures of the structure of the networks: the MIB seems characterized by medium size holders controlling separate subsets of stocks, while the US markets seem characterized by very large holders sharing control over subsets of stocks. © 2005 Springer-Verlag Berlin Heidelberg
Financial fragility and distress propagation in a network of regions
Full text linkWe investigate how the financial fragility in the real economy is affected by the average level of interdependence among agents across different regions of the economy. To this end, we develop a parsimonious agent-based model of firms and banks organized in geographic regions. The model is built on the framework of an existing class of models for business fluctuations. The goal of our exercise is to clarify the effect on systemic failures of the interplay between network interconnectedness and financial acceleration. In particular, we investigate the probability of individual and systemic failures with varying levels of interconnectedness. We find that, in the absence of financial acceleration, connectivity makes the system more resilient. In contrast, in the presence of financial acceleration, the probability of both individual and systemic failures are minimized at intermediate level of diversification
The Network of Global Corporate Control
Full text linkThe 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
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