IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2020-06-05T16:57:40ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2018-03-09T13:45:20Z2018-03-09T13:45:20Zhttp://eprints.imtlucca.it/id/eprint/4039This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40392018-03-09T13:45:20ZEnhanced Gravity Model of trade: reconciling macroeconomic and network modelsThe structure of the International Trade Network (ITN), whose nodes and links represent world countries and their trade relations respectively, affects key economic processes worldwide, including globalization, economic integration, industrial production, 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 connected countries, using macroeconomic properties such as GDP, geographic distance, and possibly other factors. However, it predicts a network with complete or homogeneous topology, thus failing to reproduce the highly heterogeneous structure of the 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 Gravity Model with the network approach within a maximum-entropy framework. Via a 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. The model successfully reproduces both the global topology and the local link weights of the ITN, parsimoniously reconciling the conflicting approaches. It also indicates that the probability that any two countries trade a certain volume should follow a geometric or exponential distribution with an additional point mass at zero volume.Assaf AlmogRhys BirdDiego Garlaschellidiego.garlaschelli@imtlucca.it2018-03-09T13:43:01Z2018-03-09T13:43:01Zhttp://eprints.imtlucca.it/id/eprint/4038This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40382018-03-09T13:43:01ZEnhanced capital-asset pricing model for the reconstruction of bipartite financial networksReconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets are strongly affected by the interconnections among financial institutions. Yet, while the aggregate balance sheets of institutions are publicly disclosed, information on single positions is mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of systemic risk. Moreover, reconstruction techniques are generally designed for monopartite networks of bilateral exposures between financial institutions, thus failing in reproducing bipartite networks of security holdings (e.g., investment portfolios). Here we propose a reconstruction method based on constrained entropy maximization, tailored for bipartite financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows us to reproduce the correct topology of the network. We test this enhanced CAPM (ECAPM) method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009–2015). Our approach outperforms the traditional CAPM and the recently proposed maximum-entropy CAPM both in reproducing the network topology and in estimating systemic risk due to fire sales spillovers. In general, ECAPM can be applied to the whole class of weighted bipartite networks described by the fitness model.Tiziano Squartinitiziano.squartini@imtlucca.itAssaf AlmogGuido Caldarelliguido.caldarelli@imtlucca.itIman van LelyveldDiego Garlaschellidiego.garlaschelli@imtlucca.itGiulio Ciminigiulio.cimini@imtlucca.it2018-03-09T13:15:51Z2018-03-09T13:15:52Zhttp://eprints.imtlucca.it/id/eprint/3998This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/39982018-03-09T13:15:51ZThe double role of GDP in shaping the structure of the International Trade NetworkThe International Trade Network (ITN) is the network formed by trade relationships between world countries. The complex structure of the ITN impacts important economic processes such as globalization, competitiveness, and the propagation of instabilities. Modeling the structure of the ITN in terms of simple macroeconomic quantities is therefore of paramount importance. While traditional macroeconomics has mainly used the Gravity Model to characterize the magnitude of trade volumes, modern network theory has predominantly focused on modeling the topology of the ITN. Combining these two complementary approaches is still an open problem. Here we review these approaches and emphasize the double role played by GDP in empirically determining both the existence and the volume of trade linkages. Moreover, we discuss a unified model that exploits these patterns and uses only the GDP as the relevant macroeconomic factor for reproducing both the topology and the link weights of the ITN.Assaf AlmogTiziano Squartinitiziano.squartini@imtlucca.itDiego Garlaschellidiego.garlaschelli@imtlucca.it2018-03-09T13:13:21Z2018-03-09T13:13:21Zhttp://eprints.imtlucca.it/id/eprint/3997This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/39972018-03-09T13:13:21ZUncovering functional brain signature via random matrix theoryThe brain is organized in a modular way, serving multiple functionalities. This multiplicity requires that both positive (e.g. excitatory, phase-coherent) and negative (e.g. inhibitory, phase-opposing) interactions take place across brain modules. Unfortunately, most methods to detect modules from time series either neglect or convert to positive any measured negative correlation. This may leave a significant part of the sign-dependent functional structure undetected. Here we present a novel method, based on random matrix theory, for the identification of sign-dependent modules in the brain. Our method filters out the joint effects of local (unit-specific) noise and global (system-wide) dependencies that empirically obfuscate such structure. The method is guaranteed to identify an optimally contrasted functional `signature', i.e. a partition into modules that are positively correlated internally and negatively correlated across. The method is purely data-driven, does not use any arbitrary threshold or network projection, and outputs only statistically significant structure. In measurements of neuronal gene expression in the biological clock of mice, the method systematically uncovers two otherwise undetectable, negatively correlated modules whose relative size and mutual interaction strength are found to depend on photoperiod. The neurons alternating between the two modules define a candidate region of functional plasticity for circadian modulation.Assaf AlmogOri RoethlerRenate BuijinkStephan MichelJohanna H MeijerJos H T RohlingDiego Garlaschellidiego.garlaschelli@imtlucca.it2015-11-05T13:51:23Z2018-03-08T16:57:47Zhttp://eprints.imtlucca.it/id/eprint/2826This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28262015-11-05T13:51:23ZA GDP-driven model for the binary and weighted structure of the International Trade NetworkRecent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among economists) and the fitness model (more popular among networks scientists), are both limited to the characterization of only one representation of the ITN. The gravity model satisfactorily predicts the volume of trade between connected countries, but cannot reproduce the missing links (i.e. the topology). On the other hand, the fitness model can successfully replicate the topology of the ITN, but cannot predict the volumes. This paper tries to make an important step forward in the unification of those two frameworks, by proposing a new gross domestic product (GDP) driven model which can simultaneously reproduce the binary and the weighted properties of the ITN. Specifically, we adopt a maximum-entropy approach where both the degree and the strength of each node are preserved. We then identify strong nonlinear relationships between the GDP and the parameters of the model. This ultimately results in a weighted generalization of the fitness model of trade, where the GDP plays the role of a ‘macroeconomic fitness’ shaping the binary and the weighted structure of the ITN simultaneously. Our model mathematically explains an important asymmetry in the role of binary and weighted network properties, namely the fact that binary properties can be inferred without the knowledge of weighted ones, while the opposite is not true.Assaf AlmogTiziano Squartinitiziano.squartini@imtlucca.itDiego Garlaschellidiego.garlaschelli@imtlucca.it