Financial interbanking networks resilience under shocks propagation

Clustering networked funded European research activities through rank-size laws

This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007-2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years.

Financial resilience of insurance network during Covid-19 pandemic

We provide a novel approach for analysing the financial resilience of the insurance sector during coronavirus pandemic. To this end, we build temporal directed and weighted networks where the weights on the arcs take into account the tail dependence between couple of firms. To assess the resilience of the network, we provide a new global indicator, aimed at capturing the impact on the clustering coefficient of a shock affecting in turn each firm and diffusing in the network via shortest paths. A local measure of resilience is also provided by quantifying the contribution of each firm to the global indicator. In this way, we are able to detect most critical firms in the system. A numerical application has been developed in order to test the proposed approach. The results show that the proposed resilience measure appears able to detect main periods of financial crises. The first wave of COVID-19 pandemic results as a extreme phenomenon in the market and the lowest resilience is associated to the period in which COVID-19 has been declared pandemic.