247 research outputs found
Concurrent enhancement of percolation and synchronization in adaptive networks
Co-evolutionary adaptive mechanisms are not only ubiquitous in nature, but
also beneficial for the functioning of a variety of systems. We here consider
an adaptive network of oscillators with a stochastic, fitness-based, rule of
connectivity, and show that it self-organizes from fragmented and incoherent
states to connected and synchronized ones. The synchronization and percolation
are associated to abrupt transitions, and they are concurrently (and
significantly) enhanced as compared to the non-adaptive case. Finally we
provide evidence that only partial adaptation is sufficient to determine these
enhancements. Our study, therefore, indicates that inclusion of simple adaptive
mechanisms can efficiently describe some emergent features of networked
systems' collective behaviors, and suggests also self-organized ways to control
synchronization and percolation in natural and social systems.Comment: Published in Scientific Report
DebtRank: A microscopic foundation for shock propagation
The DebtRank algorithm has been increasingly investigated as a method to
estimate the impact of shocks in financial networks, as it overcomes the
limitations of the traditional default-cascade approaches. Here we formulate a
dynamical "microscopic" theory of instability for financial networks by
iterating balance sheet identities of individual banks and by assuming a simple
rule for the transfer of shocks from borrowers to lenders. By doing so, we
generalise the DebtRank formulation, both providing an interpretation of the
effective dynamics in terms of basic accounting principles and preventing the
underestimation of losses on certain network topologies. Depending on the
structure of the interbank leverage matrix the dynamics is either stable, in
which case the asymptotic state can be computed analytically, or unstable,
meaning that at least one bank will default. We apply this framework to a
dataset of the top listed European banks in the period 2008 - 2013. We find
that network effects can generate an amplification of exogenous shocks of a
factor ranging between three (in normal periods) and six (during the crisis)
when we stress the system with a 0.5% shock on external (i.e. non-interbank)
assets for all banks.Comment: 10 pages, 2 figure
A review of blind source separation in NMR spectroscopy
27 pagesInternational audienceFourier transform is the data processing naturally associated to most NMR experiments. Notable exceptions are Pulse Field Gradient and relaxation analysis, the structure of which is only partially suitable for FT. With the revamp of NMR of complex mixtures, fueled by analytical challenges such as metabolomics, alternative and more apt mathematical methods for data processing have been sought, with the aim of decomposing the NMR signal into simpler bits. Blind source separation is a very broad definition regrouping several classes of mathematical methods for complex signal decomposition that use no hypothesis on the form of the data. Developed outside NMR, these algorithms have been increasingly tested on spectra of mixtures. In this review, we shall provide an historical overview of the application of blind source separation methodologies to NMR, including methods specifically designed for the specificity of this spectroscopy
Evolution of controllability in interbank networks
The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected “hub” institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies
The scale-free topology of market investments
We propose a network description of large market investments, where both
stocks and shareholders are represented as vertices connected by weighted links
corresponding to shareholdings. In this framework, the in-degree () and
the sum of incoming link weights () of an investor correspond to the number
of assets held (\emph{portfolio diversification}) and to the invested wealth
(\emph{portfolio volume}) respectively. An empirical analysis of three
different real markets reveals that the distributions of both and
display power-law tails with exponents and . Moreover, we find
that scales as a power-law function of with an exponent .
Remarkably, despite the values of , and differ across
the three markets, they are always governed by the scaling relation
. We show that these empirical findings can be
reproduced by a recent model relating the emergence of scale-free networks to
an underlying Paretian distribution of `hidden' vertex properties.Comment: Final version accepted for publication on Physica
Pathways towards instability in financial networks
Following the financial crisis of 2007–2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the details of how financial institutions interact typically play a decisive role, and a general understanding of precisely how network topology creates instability remains lacking. Here we show how processes that are widely believed to stabilize the financial system, that is, market integration and diversification, can actually drive it towards instability, as they contribute to create cyclical structures which tend to amplify financial distress, thereby undermining systemic stability and making large crises more likely. This result holds irrespective of the details of how institutions interact, showing that policy-relevant analysis of the factors affecting financial stability can be carried out while abstracting away from such details
Portfolio diversification, differentiation and the robustness of holdings networks
Abstract Networks of portfolio holdings exemplify how interdependence both between the agents and their assets can be a source of systemic vulnerability. We study a real-world holdings network and compare it with various alternative scenarios from randomization and rebalancing of the original investments. Scenarios generation relies on algorithms that satisfy the global constraints imposed by the numbers of outstanding shares in the market. We consider fixed-diversification models and diversification-maximizing replicas too. We extensively analyze the interplay between portfolio diversification and differentiation, and how the outreach of exogenous shocks depends on these factors as well as on the type of shock and the size of the network with respect to the market. We find that real portfolios are poorly diversified but highly similar, that portfolio similarity correlates with systemic fragility and that rebalancing can come with an increased similarity depending on the initial network configuration. We show that a large diversification gain is achieved through rebalancing but, noteworthy, that makes the network vulnerable in front of unselective shocks. Also, while the network is riskier in the presence of targeted shocks, it is safer than its random counterparts when it is stressed by widespread price downturns
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