Heterogeneity and clustering of defaults

This paper studies how the degree of heterogeneity among hedge funds' demand orders for a risky asset affects the possibility of their defaults being clustered. We find that fire-sales caused by margin calls is a necessary, yet not a sufficient condition for defaults to be clustered. We show that when the degree of heterogeneity is sufficiently high, poorly performing HFs are able to obtain a higher than usual market share, which leads to an improvement of their performance. Consequently, their survival time is prolonged, increasing the probability of them remaining in operation until the downturn of the next leverage cycle. This leads to an increase in the probability of poorly and high-performing hedge funds to default in sync at a later time, and thus also in the probability of collective defaults. Our analytical results establish a connection between the nontrivial aggregate statistics and the presence of infinite memory in the process governing the hedge funds' defaults

Hedging against risk in a heterogeneous leveraged market

This paper provides a theoretical model which highlights the role of heterogeneity of information in the emergence of temporal aggregation (clustering) of defaults in a leveraged economy. We show that the degree of heterogeneity plays a critical role in the persistence of the correlation between defaults in time. Specifically, a high degree of heterogeneity leads to an autocorrelation of the time sequence of defaults characterised by a hyperbolic decay rate, such that the autocorrelation function is not summable (infinite memory) and defaults are clustered. Conversely, if the degree of heterogeneity is reduced the autocorrelation function decays exponentially fast, and thus, correlation between defaults is only transient (short memory). Our model is also able to reproduce stylized facts, such as clustered volatility and non-Normal returns. Our findings suggest that future regulations might be directed at improving publicly available information, reducing the relative heterogeneity

Heterogeneity and clustering of defaults

This paper provides a theoretical model which highlights the role of heterogeneity of information in the emergence of temporal aggregation (clustering) of defaults in a leveraged economy. We show that the degree of heterogeneity plays a critical role in the persistence of the correlation between defaults in time. Specifically, a high degree of heterogeneity leads to an autocorrelation of the time sequence of defaults characterised by a hyperbolic decay rate, such that the autocorrelation function is not summable (infinite memory) and defaults are clustered. Conversely, if the degree of heterogeneity is reduced the autocorrelation function decays exponentially fast, and thus, correlation between defaults is only transient (short memory). Our model is also able to reproduce stylized facts, such as clustered volatility and non-Normal returns. Our findings suggest that future regulations might be directed at improving publicly available information, reducing the relative heterogeneity

Hedging against Risk in a Heterogeneous Leveraged Market

This paper focuses on the use of interest rates as a tool for hedging against the default risk of heterogeneous hedge funds (HFs) in a leveraged market. We assume that the banks study the HFs survival statistics in order to compute default risk and hence the correct interest rate. The emergent non-trivial (heavy-tailed) statistics observed on the aggregate level, prevents the accurate estimation of risk in a leveraged market with heterogeneous agents. Moreover, we show that heterogeneity leads to the clustering of default events and constitutes thus a source of systemic risk

Graph Rewriting Rules for RDF Database Evolution Management

International audienceThis paper introduces SetUp, a theoretical and applied framework for the management of RDF/S database evolution on the basis of graph rewriting rules. Rewriting rules formalize instance or schema changes, ensuring graph’s consistency with respect to given constraints. Constraints considered in this paper are a well known variant of RDF/S semantic, but the approach can be adapted to user-defined constraints. Furthermore, SetUp manages updates by ensuring rule applicability through the generation of side-effects: new updates which guarantee that rule application conditions hold.We provide herein formal validation and experimental evaluation of SetUp

National records of 3000 European bee and hoverfly species: A contribution to pollinator conservation

Pollinators play a crucial role in ecosystems globally, ensuring the seed production of most flowering plants. They are threatened by global changes and knowledge of their distribution at the national and continental levels is needed to implement efficient conservation actions, but this knowledge is still fragmented and/or difficult to access. As a step forward, we provide an updated list of around 3000 European bee and hoverfly species, reflecting their current distributional status at the national level (in the form of present, absent, regionally extinct, possibly extinct or non-native). This work was attainable by incorporating both published and unpublished data, as well as knowledge from a large set of taxonomists and ecologists in both groups. After providing the first National species lists for bees and hoverflies for many countries, we examine the current distributional patterns of these species and designate the countries with highest levels of species richness. We also show that many species are recorded in a single European country, highlighting the importance of articulating European and national conservation strategies. Finally, we discuss how the data provided here can be combined with future trait and Red List data to implement research that will further advance pollinator conservation