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Found 11 papers, 0 papers with code
A Look at Financial Dependencies by Means of Econophysics and Financial Economics
In particular we show that a useful way to describe dependencies is by means of information filtering networks that are able to retrieve relevant and meaningful information in complex financial data sets.
Multiscaling and rough volatility: an empirical investigation
We find that the model is able to reproduce multiscaling features of the prices' time series when a low value of the Hurst exponent is used but it fails to reproduce what the real data say.
The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool
These GHE patterns, distinguish in a statistically robust way, not only between time periods of uniscaling and multiscaling, but also among different types of multiscaling: symmetric multiscaling (M) and asymmetric multiscaling (A).
A new multilayer network construction via Tensor learning
Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems.
Predicting Multidimensional Data via Tensor Learning
In particular, in this paper, we propose a parsimonious tensor regression model that retains the intrinsic multidimensional structure of the dataset.
On the statistics of scaling exponents and the Multiscaling Value at Risk
We show that by using this statistical procedure in combination with the robustly estimated multiscaling exponents, the one year forecasted MSVaR mimics the VaR on the annual data for the majority of the stocks analyzed.
Systemic liquidity contagion in the European interbank market
In this paper, we propose a model which relies on an epidemic model which simulate a contagion on the interbank market using the funding liquidity shortage mechanism as contagion process.
Parsimonious modeling with Information Filtering Networks
We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors.
Network Filtering for Big Data: Triangulated Maximally Filtered Graph
TMFG uses as weights any arbitrary similarity measure to arrange data into a meaningful network structure that can be used for clustering, community detection and modeling.
Nested hierarchies in planar graphs
We construct a partial order relation which acts on the set of 3-cliques of a maximal planar graph G and defines a unique hierarchy.
Geometric Topology
A tool for filtering information in complex systems
We introduce a technique to filter out complex data-sets by extracting a subgraph of representative links.
Disordered Systems and Neural Networks Statistical Mechanics Physics and Society
