Critical volatility triggers log-normal to power-law transitions in interconnected systems.
problem Understanding the transition from log-normal to power-law distributions in interconnected systems.
method Analyzing an infinite option-on-option chain model, deriving a critical volatility threshold.
result A critical volatility threshold of approximately 250.66% for unconditional cases, dropping to 125.3% with selective survival.
Solves model order selection for spectral graph clustering.
problem Automated selection of the correct number of clusters in spectral graph clustering.
method AMOS, a selection criterion based on asymptotic phase transition analysis.
result Validates phase transition analysis and model selection procedure on real-world data.
The paper analyzes risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
problem Interconnected risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
method Empirical analysis using OLS regression, Granger-causality, and random forest feature importance tests.
result Revenue is primarily driven by value locked in the ecosystem, yield of liquid restaking token, and multi-blockchain expansion.
We test the hypothesis that interconnections across financial institutions can be explained by a diversification motive. This idea stems from the empirical evidence of the existence of long-term exposures that cannot be explained by a liquidity motive (maturity or currency mismatch). We model endogenous interconnection…
We show that the Bregman divergence provides a rich framework to estimate unnormalized statistical models for continuous or discrete random variables, that is, models which do not integrate or sum to one, respectively. We prove that recent estimation methods such as noise-contrastive estimation, ratio matching, and sco…
A new pruning method reduces DNN size and interconnectivity using brain network principles.
problem Over-parameterization in deep neural networks causes memory and hardware cost issues.
method Structural pruning scheme based on Small-World model, trimming network before training.
result Reduced model size by 2.3% on LeNet-5 for MNIST and 9.02% on VGG-16 for CIFAR-10.
This paper models and evaluates contagion and stabilisation in interconnected financial markets.
problem Understanding and managing contagion and resilience in multilayer financial networks.
method Formulates an interconnected multiplex structure, models contagion mechanism, and designs minimum-cost stabilisation strategies.
result Empirically validated minimum-cost stabilisation strategies for multichannel contagion containment.
This paper forecasts renewable energy prospects in South America through cross-border interconnection.
problem Lack of renewable energy integration across South American countries.
method Long-term scenario forecasting methodology applied to raw data from typical countries.
result Promoting cross-border interconnection towards renewables can optimize energy supply, reduce costs, and balance the energy matrix.
EM-GAN uses GANs for fast stress analysis of multi-segment interconnects.
problem Fast and accurate stress analysis for EM failure assessment in multi-segment interconnects.
method Conditional GAN model trained on images of multi-segment wires and current densities.
result EM-GAN provides accurate stress distribution with 6.6% error and 8.3X speedup.
Model assesses systemic risk in interconnected financial systems.
problem Systemic risk in interconnected financial systems.
method Balance-sheet consistent valuation model for interbank claims.
result Existence and uniqueness of optimal valuations for all banks.
We develop a probabilistic framework for sequential random projection.
problem Challenges of sequential decision-making under uncertainty.
method Novel construction of a stopped process and method of mixtures.
result Achieved a non-asymptotic probability bound for random projection.
Explains deep learning models and their geometric properties.
problem Understanding the geometric intuition behind deep learning models.
method Geometrical intuition and novel insights into loss surfaces of deep learning models.
result Deep neural networks carve out manifolds with multiplication neurons.
Hierarchical randomized smoothing improves model robustness for complex data.
problem Certifying robustness on complex data (e.g. images, graphs) is challenging.
method Add random noise to a randomly selected subset of entities in a hierarchical manner.
result Hierarchical randomized smoothing yields stronger robustness guarantees with high accuracy.
A modern version of Monetary Circuit Theory with a particular emphasis on stochastic underpinning mechanisms is developed. It is explained how money is created by the banking system as a whole and by individual banks. The role of central banks as system stabilizers and liquidity providers is elucidated. It is shown how…
In this paper, we study the sensitivity of the spectral clustering based community detection algorithm subject to a Erdos-Renyi type random noise model. We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probability under a general…
New framework for 3D spatial topology enumeration and identification.
problem Efficient navigation through complex engineering system topologies.
method Mathematical spatial graph theory to represent, enumerate, and identify unique topological classes.
result Identification of distinctive 3D topological classes for engineering systems.
Cryptocurrencies are becoming more linked in their returns and volatilities.
problem Understanding the increasing interconnectivity of cryptocurrencies.
method Examined market linkages using returns and volatilities, applied various methodologies.
result Significant increase in market linkages for both returns and volatilities.
Flash crash on Ethereum shows social coordination can destabilize blockchain systems.
problem Interconnected socio-technical systems can be fragile due to social coordination.
method Analyzed the 2017 Ethereum flash-crash to illustrate the fragility of blockchain systems.
result Emergent centralization can lead to catastrophic cascades in interconnected systems.
Bayesian networks model financial contagion in interconnected institutions.
problem Understanding and predicting contagion in financial networks.
method Structural default model using Bayesian network methodologies.
result Bayesian networks can detect contagion channels and measure systemic importance.
Low-complexity spiking networks learn complex tasks with minimal trainable parameters.
problem Training complex reinforcement learning tasks with minimal resources.
method Reinforcement learning on simple networks of spiking neurons with random connections.
result Small random spiking networks achieve learning efficiency similar to humans on complex tasks.
CoCos can increase financial fragility in certain network structures.
problem The effectiveness of CoCos in enhancing financial stability depends on the network structure.
method Analysis of phase transitions in a network of interconnected banks.
result CoCos can increase financial fragility under certain network structures.
BOIS optimizes complex systems by combining known and unknown functions, improving efficiency.
problem Optimizing complex systems with limited structural knowledge.
method Adaptive linearization of composite functions using Gaussian Process models.
result BOIS outperforms existing grey-box methods in efficiency and effectiveness.
This study examines global financial market interconnections among 4,000 stocks from 15 countries.
problem Local financial shocks can amplify into global events in a highly interconnected financial system.
method Normalized returns using GARCH model and robust regression to estimate pairwise statistical relationships.
result The United States and Germany are central to the global stock market, and energy and materials sectors play a significant role in connecting markets.
BOIS optimizes complex systems by leveraging structural knowledge.
problem Optimizing complex systems with black-box models and structural knowledge.
method Adaptive linearization of composite functions to exploit structural knowledge.
result BOIS achieves performance gains and accurately captures composite function statistics.
Measures systemic risk in interconnected financial firms.
problem Contagion effects and systemic risk in financial networks.
method Formal computation of sensitivities (Greeks) in network context.
result Proposes network Δ as a measure of systemic risk. RiskRank measures interconnected systemic risk.
problem Measuring systemic risk in interconnected systems.
method RiskRank function aggregating risk measures considering both individual and interrelated risks.
result RiskRank performs well in out-of-sample analysis of systemic risk.
Model analyzes default risk in interconnected banking networks with jumps.
problem Analyzing default risk in interconnected banking networks with jumps.
method Developed a finite difference method for a two-dimensional partial integro-differential equation, studied stability and consistency, computed survival probabilities and CDS prices.
result Calibrated model to market data and assessed the impact of jump risk.
AMOS automates model order selection for spectral graph clustering.
problem Automated selection of the correct number of clusters in spectral graph clustering.
method Incrementally increases the number of clusters, estimates cluster quality, and provides reliability tests.
result AMOS outputs clusters of minimal model order with statistical guarantees.
Study identifies disturbance location and magnitude in power systems.
problem Identifying the location and magnitude of disturbances in interconnected power systems.
method Model-free approach using frequency data from generators; logistic regression for localization, linear regression for magnitude estimation.
result Achieves highly accurate localization and estimation performance in the presence of noise and missing data.
Modeling financial networks to predict systemic crises.
problem Predicting systemic financial crises in complex networks.
method Developed inhomogeneous random financial networks (IRFNs) to model bank interactions.
result Found a condition for a locally tree-like independence (LTI) property, leading to fixed point equations for system equilibrium.
Proposes BGCN-NRWS for semi-supervised node classification with reduced overfitting.
problem Uncertainty in graph structure for semi-supervised node classification.
method Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS) with MCMC graph sampling and variational inference.
result Consistently competitive classification results compared to state-of-the-art.
IntraLayer connects digital finance platforms efficiently.
problem Inefficient interconnectivity in digital finance.
method Core infrastructure and adaptive fiscal policy.
result Optimised transactional efficiency for various agents.
Enhanced CAPM reconstructs financial network topology and systemic risk.
problem Reconstructing financial interconnection patterns from partial information.
method Constrained entropy maximization tailored for bipartite financial networks.
result ECAPM outperforms traditional CAPM and MECAPM in network topology and systemic risk estimation.
Optimal interbank lending scheme with probabilistic bank failure constraints.
problem Optimizing interbank lending in a network of interconnected banks with probabilistic constraints on failure.
method Derive a closed-form solution for an optimal control problem, compute systemic relevance parameters.
result General solution for interbank lending with probabilistic constraints for all banks.
Investors with asymmetric information play a game to optimize their portfolios.
problem Two investors with different information levels compete in portfolio selection.
method Modelled as a Stackelberg game with entropy-regularized mean-variance objectives.
result Equilibria exist where follower's strategy depends on leader's actions.
System recommends workouts and predicts success rates using RNNs.
problem Promoting healthy lifestyles through personalized exercise recommendations.
method Two interconnected recurrent neural networks (RNNs) using historical workout data.
result Interconnected-RNN model predicts exercise success rates with improved accuracy.
In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply …
BWFlow improves graph generation by smoothly interpolating graph components.
problem Disjoint modeling of graph nodes and edges leads to irregular and non-smooth probability paths.
method Modeling graphs as MRFs and using optimal transport displacement for a smooth probability path.
result BWFlow achieves better training convergence and efficient sampling in graph generation.
AMPNet accelerates deep learning training on interconnected devices.
problem Training efficiency for dynamic neural networks with complex control flow.
method Asynchronous model-parallel training algorithm.
result AMP training achieves similar accuracy but faster convergence and shorter training times.
New method uses consistent gradient estimators for SGD in interconnected graphs.
problem Efficiently computing unbiased gradient estimators in interconnected graph scenarios.
method Uses consistent gradient estimators as an alternative to unbiased ones.
result Consistent estimators lead to the same convergence behavior as unbiased ones in various objective types.
Research explores how interconnected systems synchronize and how to control their behavior.
problem Understanding and controlling the behavior of interconnected dynamical systems.
method Mean field games approach applied to controlled coupled oscillators.
result Developed methods to predict and influence emergent phenomena in interconnected systems.
Analyzed banking crises across 66 countries, showing interconnections and clustering patterns.
problem Characterizing banking crises and their interconnections across different countries.
method Used dichotomous banking crises time series data from 1800 to 2014, analyzed via heatmap matrices and clustering.
result Countries exhibit pairwise correlation in banking crises, and crises tend to affect countries with financial links.
The paper examines expectile quadrangle properties in risk management.
problem Exploring the properties of expectile quadrangles in risk management.
method Rigorously examines the properties of expectile quadrangles.
result Rigorously examines the properties of expectile quadrangles.
COKE reduces communication in decentralized kernel learning.
problem Decentralized kernel learning challenges due to data-dependent decision variables.
method Random feature approximation and ADMM for iterative optimization; communication-censored algorithm.
result COKE achieves faster convergence and reduced communication load.
Paper develops a new fluid flow model with energy exchange through boundaries.
problem Modeling ideal fluid flow with energy exchange through boundaries.
method Port-Hamiltonian model based on Stokes-Dirac structures.
result Wide range of fluid dynamical systems can be achieved with this model.
This study quantifies systemic risk from overlapping portfolios in the Mexican financial system.
problem Systemic risk from indirect interconnections between financial institutions.
method Represented the Mexican financial system as a bipartite network of securities and financial institutions; quantified systemic risk from overlapping portfolios.
result Total systemic risk levels underestimated by up to 50% when only direct exposures are considered.
UK imports face higher volatility during high demand periods.
problem Price volatility and national sovereignty during high demand.
method Analysis of early 2017 conditions in France, Germany, and UK.
result National interests take precedence over environmental sustainability.
We analyze the Levy processes produced by means of two interconnected classes of non stable, infinitely divisible distribution: the Variance Gamma and the Student laws. While the Variance Gamma family is closed under convolution, the Student one is not: this makes its time evolution more complicated. We prove that -- a…