9 research outputs found
Complex Correlation Approach for High Frequency Financial Data
We propose a novel approach that allows to calculate Hilbert transform based
complex correlation for unevenly spaced data. This method is especially
suitable for high frequency trading data, which are of a particular interest in
finance. Its most important feature is the ability to take into account
lead-lag relations on different scales, without knowing them in advance. We
also present results obtained with this approach while working on Tokyo Stock
Exchange intraday quotations. We show that individual sectors and subsectors
tend to form important market components which may follow each other with small
but significant delays. These components may be recognized by analysing
eigenvectors of complex correlation matrix for Nikkei 225 stocks.
Interestingly, sectorial components are also found in eigenvectors
corresponding to the bulk eigenvalues, traditionally treated as noise
Detectability of Macroscopic Structures in Directed Asymmetric Stochastic Block Model
We study the problem of identifying macroscopic structures in networks,
characterizing the impact of introducing link directions on the detectability
phase transition. To this end, building on the stochastic block model, we
construct a class of hardly detectable directed networks. We find closed form
solutions by using belief propagation method showing how the transition line
depends on the assortativity and the asymmetry of the network. Finally, we
numerically identify the existence of a hard phase for detection close to the
transition point.Comment: 9 pages, 7 figure
Network Sensitivity of Systemic Risk
A growing body of studies on systemic risk in financial markets has
emphasized the key importance of taking into consideration the complex
interconnections among financial institutions. Much effort has been put in
modeling the contagion dynamics of financial shocks, and to assess the
resilience of specific financial markets - either using real network data,
reconstruction techniques or simple toy networks. Here we address the more
general problem of how shock propagation dynamics depends on the topological
details of the underlying network. To this end we consider different realistic
network topologies, all consistent with balance sheets information obtained
from real data on financial institutions. In particular, we consider networks
of varying density and with different block structures, and diversify as well
in the details of the shock propagation dynamics. We confirm that the systemic
risk properties of a financial network are extremely sensitive to its network
features. Our results can aid in the design of regulatory policies to improve
the robustness of financial markets
Vulnerabilities of democratic electoral systems
Trabajo presentado en la Conference on Complex Systems (CCS), celebrada en Lyon del 25 al 29 de octubre de 2021.The vulnerability of democratic processes is under scrutiny after scandals related to Cambrige Analytica (2016 U.S. elections, the Brexit referendum, and elections in Kenya [1]). The deceptive use of social media in the US, the European Union and several Asian countries, increased social and political polarization across world regions. Finally, there are straightforward frauds like Crimea referendum and Belarus elections. These challenges are eroding democracy, the most frequent source of governmental power, and raise multiple questions about its vulnerabilities [2]. Democratic systems have countless ways of performing elections, which create different electoral systems (ES). It is therefore in citizens’ interest to study and understand how different ESs relate to different vulnerabilities and contemporary challenges. These systems can be analyzed using network science in various layers – they involve a network of voters in the first place, a network of electoral districts connected by commuting flow for instance, or a network of political parties to give a few examples. The electoral system together with the underlying voting processes and opinion dynamics can be seen as a complex system [3].
We study electoral systems in a dynamical framework. We look at the volatility of the election results, analyzing how much they vary over time. However, the term volatility is frequently used in relation to the Pedersen index of volatility. In this meaning it has been studied and even linked to the party system instability [4, 5]. Our approach goes far beyond two-point volatility. We analyze the vulnerability of an ES based on a long run of opinion dynamics process with many elections performed during the evolution. In this context, we consider that a system is more vulnerable, if it has a larger variance of the election results and if it magnifies the influence of extremism and media. We can further identify which voting system is more sensitive to fluctuations, and which one is more
vulnerable to internal/external influences, like zealots or propaganda. This allows us to construct a probability distribution of election results under every electoral system. It is essential to provide new tools and arguments to the discussion on the evaluation of electoral systems. We aim at comparing different ESs in a dynamical framework. Our novel approach of analyzing electoral systems in such way with all its aspects included, from opinion dynamics in the population of voters to inter-district commuting patterns to seat appointment methods, will help answering questions like: Which electoral systems are more predictable/stable under fluctuations? Which electoral systems are the most robust (or vulnerable) under external and internal influences? Which features of electoral systems make them more (less) stable
Supplementary Materials from Congruity of genomic and epidemiological data in modelling of local cholera outbreaks
Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have used cholera case count data. More recently, whole-genome sequence data have qualitatively described cholera transmission. Integrating these data streams may provide much more accurate models of cholera spread; however, no systematic analyses have been performed so far to compare traditional case-count models to the phylodynamic models from genomic data for cholera transmission. Here, we use high-fidelity case count and whole-genome sequencing data from the 1991 to 1998 cholera epidemic in Argentina to directly compare the epidemiological model parameters estimated from these two data sources. We find that phylodynamic methods applied to cholera genomics data provide comparable estimates that are in line with established methods. Our methodology represents a critical step in building a framework for integrating case-count and genomic data sources for cholera epidemiology and other bacterial pathogens
Electoral Systems Simulations (v1.0.0)
This version of the repository is our first release. It contains the simulation code as used in our first paper