Abstract: There are two fundamentally different networks in play in global trade networks: the trade in goods and the trade in services. These two complementary networks describe the way in which economic resources are dynamically allocated within countries, local regions and across the globe. It is interesting to observe that these networks are the macroscopic consequences of the detailed microstructure of the trade in goods and services between individual market sectors. It is only recently that analysis of such detailed microstructure and the network-to-network exchange of economic resources at multiple scales has been made possible through the development of econometrically consistent ‘trade in value added’ data has become readily available to the broader research community. In an exploratory analysis of global economic trade, we combine well established complex network theory with the newly emerging methods from the ‘networks of networks’ field to uncover a rich diversity of interactions at multiple scales both within the networks and between the networks. While it is now well understood that individual networks have specific risks associated with specific network topologies, for example single node vulnerabilities pose different network risks for scale-free versus Erdos-Renyi networks, it is a more complex issue when considering interactions between networks. In this study we use the OECD’s trade in value added data set to study the capital flows that are exchanged between networks that give rise to specific risks that are not immediately apparent when the networks are considered in isolation. As the long term goals of such analysis needs to be to inform the debate of global economic risks, we conclude by discussing some of the practical consequences for our understanding of global economic trade.

Abstract: The International Trade Network (ITN) is involved in an increasing number of processes of relevance for the world economy, including globalization, integration, competitiveness, and the propagation of shocks and instabilities. Characterizing the ITN via a simple yet accurate model is an open problem. The traditional Gravity Model successfully reproduces the volume of trade between two connected countries, using macroeconomic properties such as GDP and geographic distance. However, it generates a network with a complete or homogeneous topology, thus failing to reproduce the highly heterogeneous structure of the real ITN. On the other hand, recent maximum-entropy network models successfully reproduce the complex topology of the ITN, but provide no information about trade volumes. Here we integrate these two currently incompatible approaches via the introduction of an Enhanced Gravity Model (EGM) of trade. The EGM is the simplest model combining the maximum-entropy network approach with the Gravity Model, while at the same time enforcing a novel ingredient that we denote as `topological invariance', i.e. the invariance of the expected topology under an arbitrary change of units of trade volumes. Via this unified and principled mechanism that is transparent enough to be generalized to any economic network, the EGM provides a new econometric framework wherein trade probabilities and trade volumes can be separately controlled by any combination of dyadic and country-specific macroeconomic variables. We show that the EGM successfully reproduces both the topology and the weights of the ITN, finally reconciling the conflicting approaches. Moreover, it provides a general and simple theoretical explanation for the failure of economic models that do not explicitly focus on network topology: namely, their lack of topological invariance.

Abstract: This paper analyzes the world web of mergers and acquisitions (M\&As) using a complex network approach. We aggregate data of M\&As to build a temporal sequence of binary and weighted-directed networks, for the 1995-2010 period and 224 countries. We study different geographical and temporal aspects of the M\&As web, building sequences of filtered sub-networks which links belongs to specific intervals of distance or time. Then, we derive directed-network statistics to see how topological properties of the network change over space and time. The M\&A web is a low density network characterized by a persistent giant component with many external nodes and with a few number of reciprocated links. Clustering patterns are very heterogeneous and dynamic. High-income economies are characterized by high connectivity, these countries mainly merge to several high- and middle-income economies, implying that most countries might work as targets of a few acquirers. We find that distance strongly impacts the structure of the network: link-weights and node degrees are non-linear.

Abstract: Financial Agent Based Models (ABM) have been developed aiming at understanding the Stylized Facts (SF) observed in the financial time series. ABM allowed to overcome mainstream's vision and concepts like the rational representative agent. ABMs are capable of explaining the role of elements such as heterogeneity of strategies and time horizons, contagion dynamics, intrinsic large fluctuations (endogenous) but they still can not be concretely useful in policy-making processes. In a series of papers regarding a minimal ABM [see Alfi V., et al. Eur. Phys. J. B, 67 (2009) 385] it is shown that a key element in order to measure systemic risk and financial distress is the effective number of agents or, in other words, the number of effectively independent strategies in the market. In order to verify this insight, we tried to develop strategies to empirically estimate this coherence. We discuss some preliminary results of a novel measure of the stock market coherence with a reference community for this research. A market becomes coherent when agents (i.e investing subjects) tend to behave similarly and consequently perform the same actions. In such a scenario, markets are maximally exposed to large positive feedbacks and self-reinforcing dynamics which can dramatically enhance even small/local financial shocks turning them into systemic and global crashes. Here we propose a simple stochastic model which allows to give a daily estimation of markets' coherence starting from the modeling of the correlation network among stocks. The parameters of the model are estimated via Monte-Carlo procedure applied to daily price time series. This measure is a promising index to assess systemic risk of financial systems. The measure does not simply reproduce standard risk measures as the realized and implied volatility and it especially appears to be informative on the building dynamics of events before financial crisis.