Systemic risk assessment through high order clustering coefficient

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 Cerqueti et al. (2018). 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.Comment: Submitte

Market Dynamics When Agents Anticipate Correlation Breakdown

The aim of this paper is to analyse the effect introduced in the dynamics of a financial market when agents anticipate the occurrence of a correlation breakdown. What emerges is that correlation breakdowns can act both as a consequence and as a triggering factor in the emergence of financial crises rational bubbles. We propose a market with two kinds of agents: speculators and rational investors. Rational agents use excess demand information to estimate the variance-covariance structure of assets returns, and their investment decisions are represented as a Markowitz optimal portfolio allocation. Speculators are uninformed agents and form their expectations by imitative behavior, depending on market excess demand. Several market equilibria result, depending on the prevalence of one of the two types of agents. Differing from previous results in the literature on the interaction between market dynamics and speculative behavior, rational agents can generate financial crises, even without the speculator contribution

Corporate board network and information flows in the Italian Stock Exchange

EnIn this work we investigate the governance models of listed companies in the Italian Stock Exchange. We use a network approach in order to investigate the interlinks among board of directors. A graph theoretical approach and the vertex centrality measures quantify the role of network and the intensity of the relationships, according to the assumption that the information conveyed by such interconnections could exert some impact on market prices. A weighted graph represent the listed companies (vertices) and their relationships (weighted edges) by using as centrality measure the flow-betweenness. It is expected that this centrality measure may give a representation of the intensity of the relationship between companies, capturing the volume of information flowing from a vertex to another one

Taxonomy of Cohesion Coefficients for Weighted and Directed Multilayer Networks

Clustering and closure coefficients are among the most widely applied indicators in the description of the topological structure of a network. Many distinct definitions have been proposed over time, particularly in the case of weighted networks, where the choice of the weight attributed to the triangles is a crucial aspect. In the present work, in the framework of weighted directed multilayer networks, we extend the classical clustering and closure coefficients through the introduction of the clumping coefficient, which generalizes them to incomplete triangles of any type. We then organize the class of these coefficients in a systematic taxonomy in the more general context of weighted directed multilayer networks. Such cohesion coefficients have also been adapted to the different scales that characterize a multilayer network, in order to grasp their structure from different perspectives. We also show how the tensor formalism allows incorporating the new definitions, as well as all those existing in the literature, in a single unified writing, in such a way that a suitable choice of the involved adjacency tensors allows obtaining each of them. Finally, through some applications to simulated networks, we show the effectiveness of the proposed coefficients in capturing different peculiarities of the network structure on different scales

Community structure in the World Trade Network based on communicability distances

In this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short and long-range interactions, and hence by the distance, between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific modularity function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.Comment: 40 pages, 19 figure

Multi-criteria community detection in International Trade Network

Understanding the community structure has great importance for economic analysis. Communities are characterized by properties different from those of both the individual node and the whole network and they affect various processes on the network. We combine community detection with specific topological indicators. As a result, a new weighted network is constructed by the original one, in which weights are determined taking into account all the topological indicators in a multi-criteria approach. We introduce a new algorithm to detect communities by solving the NP-hard CP-problem

DNA templated fluorescent gold nanoclusters reduced by Good’s buffer: from blue emitting seeds to red and near infrared emitters

The final published version is available at NRC Research Press via https://doi.org/10.1139/cjc-2014-0600DNA-templated fluorescent gold nanoclusters (AuNCs) have been recently prepared showing higher photostability than the silver counterpart. In this work, we examined the effect of pH, DNA length, DNA sequence, and reducing agent. Citrate, HEPES, and MES produce blue emitters, glucose and NaBH4 cannot produce fluorescent AuNCs, while ascorbate shows blue emission even in the absence of DNA. This is the first report of using Good’s buffer for making fluorescent AuNCs. Dimethylamine borane (DMAB) produces red emitters. Poly-C DNA produces AuNCs only at low pH and each DNA chain can only bind to a few gold atoms, regardless of the DNA length. Otherwise, large nonfluorescent gold nanoparticles (AuNPs) are formed. Each poly-A DNA might template a few independent AuNCs. The blue emitters can be further reduced to form red emitters by adding DMAB. The emission color is mainly determined by the type of reducing agent instead of DNA sequence.University of Waterloo || Canadian Foundation for Innovation || Natural Sciences and Engineering Research Council || Ontario Ministry of Research and Innovation |

Community structure in the World Trade Network based on communicability distances

In this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces.Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches