Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the {\it hierarchical mutual information}, which is a generalization of the traditional mutual information, and allows to compare hierarchical partitions and hierarchical community structures. The {\it normalized} version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here, the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies, and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information. Namely, the comparison of different community detection methods, and the study of the consistency, robustness and temporal evolution of the hierarchical modular structure of networks.Comment: 14 pages and 12 figure

Inflation Differentials in the Euro Area and their Determinants – an empirical view

In this paper, we present evidence on the statistical features of observed dispersion in HICP inflation rates in the Euro area. Our descriptive exercise shows that there is still a remarkable dispersion of HICP inflation rates across the member countries. We find that most of dispersion originates in the non-traded categories of the HICP. This suggests that the main source of dispersion in countries' headline inflation rates is in those components of the HICP where non-traded goods (services, (public) goods with regulated and administered prices) are more intensely represented. We then examine the determinants of inflation differentials in a panel of the states of the Euro area in 1999–2007 using alternative classifications of this group and three different datasets. The evidence presented shows that output gaps and a proxy for price level convergence were statistically significant. On the other hand, some determinants that were found significant in previous studies (for example Honohan and Lane, 2003, 2004; ECB, 2003) has no impact on inflation in our expanded time span (e.g. exchange rate movements) The dispersion of HICP inflation is expected to increase in the coming years as the new EU member states will join the Euro area. There are some risks for these countries connected with the common monetary policy, which is adjusted more to the conditions of stabilized advanced economies forming the core of the Euro area. This creates potential problems for the EU common monetary policy (ECB), in particular negative (positive) interest rates, their repercussions on investment processes, consumption and the possibility of creating asset bubbles.http://deepblue.lib.umich.edu/bitstream/2027.42/64425/1/wp958.pd

Welfare-Enhancing Collusion in the Presence of a Competitive Fringe

Following the structure of many commodity markets, we consider a few large firms and a competitive fringe of many small suppliers choosing quantities in an infinite-horizon setting subject to demand shocks. We show that a collusive agreement among the large firms may not only bring an output contraction but also an output expansion (relative to the noncollusive output level). The latter occurs during booms, when the fringe’s market share is more important, and is due to the strategic substitutability of quantities (we will never observe an output-expanding collusion in a price-setting game). In addition and depending on the fringe’s market share the time at which maximal collusion is most difficult to sustain can be either at booms or recessions.

Are There Arbitrage Opportunities in Credit Derivatives Markets? A New Test and an Application to the Case of CDS and ASPs

This paper analyzes possible arbitrage opportunities in credit derivatives markets using selffinancing strategies combining Credit Default Swaps and Asset Swaps Packages. We present a new statistical arbitrage test based on the subsampling methodology which has lower Type I error than existing alternatives. Using four different databases covering the period from 2005 to 2009, long-run (cointegration) and statistical arbitrage analysis are performed. Before the subprime crisis, we find long-run arbitrage opportunities in 26% of the cases and statistical arbitrage opportunities in 24% of the cases. During the crisis, arbitrage opportunities decrease to 8% and 19%, respectively. Arbitrage opportunities are more frequent in the case of relatively low rated bonds and bonds with a high coupon rate.statistical arbitrage, credit derivatives, credit spreads, cointegration, subsampling

A 10,000 Mississippi Kite flock observed in Fuerte Esperanza, Argentina

The Mississippi Kite (Ictinia mississippiensis) is a Neotropical migrant that nests singly or in loose, small colonies in North America from North Carolina and north Florida west to Kansas, southeastern Colorado, and Texas and north along Mississippi River to south Illinois and Indiana, and west locally to Arizona (Brown & Amadon 1968, Thiollay 1994, Wheeler 2003). The species migrates along the Mesoamerican Land Corridor into South America where it feeds on flying insects in rather small flocks. Wintering range is very imprecisely known but major part of population is likely to winter in central South America east of the Andes, southeastern Bolivia, Paraguay to northern Argentina, and at least to some extent, southwestern Brazil, wandering occasionally to Minas Gerais (Davis 1989, Fergusson-Lees & Christie 2001, Wheeler 2003, Kirwan et al. 2004).Fil: Areta, Juan Ignacio. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina. Grupo FALCO; ArgentinaFil: Seipke, Sergio H.. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; Argentin