A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Investors prioritize ESG in crypto-assets, showing higher exposure than traditional assets.
The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.
We propose a statistical model for weighted temporal networks capable of measuring the level of heterogeneity in a financial system. Our model focuses on the level of diversification of financial institutions; that is, whether they are more inclined to distribute their assets equally among partners, or if they rather c…
In spite of the growing theoretical literature on cascades of failures in interbank lending networks, empirical results seem to suggest that networks of direct exposures are not the major channel of financial contagion. In this paper we show that networks of interbank exposures can however significantly amplify contagi…
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
This paper provides an attempt to formalize Hayek's notion of spontaneous order within the framework of the Arrow-Debreu economy. Our study shows that if a competitive economy is enough fair and free, then a spontaneous economic order shall emerge in long-run competitive equilibria so that social members together occup…
In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market…
Based on an empirical analysis of the network structure of the Austrian inter-bank market, we study the flow of funds through the banking network following exogenous shocks to the system. These shocks are implemented by stochastic changes in variables like interest rates, exchange rates, etc. We demonstrate that the sy…
Study compares two factor models for electricity spot prices across different periods.
To mitigate potential contagion from future banking crises, the European Commission recently proposed a framework which would provide for the of bank creditors in the event of failure. In this study, we examine this framework retrospectively in the context of failed European banks during the global f…
We introduce an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. Parameter families for mortality trends can be chosen freely. As model settings become high dimensional, Markov chain Monte Carlo (MCMC) is used for parameter estimation. We then link our propose…
To a large extent, the systemic importance of financial institutions is related to the topology of financial liability networks. In this work we reconstruct and analyze the - to our knowledge - largest financial network that has been studied up to now. This financial liability network consists of 51,980 firms and 796 b…
Study examines how disturbances affect financial returns in Austrian forests.
A new model synthesizes population with fewer structural and sampling zeros.
Aims to describe neural network training dynamics using two-time-scale models.
NeuPL learns diverse policies in strategy games efficiently.
Proposes a federated transfer learning method to improve precision medicine models for underrepresented populations.
New framework for online control in evolving populations.
Develops robust learning methods for datasets with sub-populations.
Estimates Mozambique's population using remote sensing and microcensus data.
Paper develops a two-population model to assess longevity basis risk.
A clustering method for multivariate populations with similar dependence structures.
In this work, we systematically investigate mean field games and mean field type control problems with multiple populations using a coupled system of forward-backward stochastic differential equations of McKean-Vlasov type stemming from Pontryagin's stochastic maximum principle. Although the same cost functions as well…
A new data set helps estimate continental-scale population distributions.
This paper studies the sample complexity of searching over multiple populations. We consider a large number of populations, each corresponding to either distribution P0 or P1. The goal of the search problem studied here is to find one population corresponding to distribution P1 with as few samples as possible. The main…
Improved sales forecasting for new products using transfer learning.
Estimates personalized policies robust to shifts in target populations.
CrowdLLM uses LLMs and generative models to create diverse digital populations.
Copula-based method generates synthetic populations from marginal distributions.
Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommend…
Existence of Radner equilibrium proven with growing population.
CTGAN synthesizes population data for travel behavior simulation.
EPC curriculum improves MARL performance as agent population grows.
Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables. The resulting diversity of neural tuning often obscures the represented information. Here we introduce a novel dimensionality reduction technique, demixed principal component analysis (dPC…
Develops a method to estimate personalized treatment regimes from summary statistics.
LEARNER improves low-rank matrix estimation using source population data.
The landscape of empirical risk has been widely studied in a series of machine learning problems, including low-rank matrix factorization, matrix sensing, matrix completion, and phase retrieval. In this work, we focus on the situation where the corresponding population risk is a degenerate non-convex loss function, nam…
Develops a weighting framework to generalize ITRs from source to target populations.
With the development of high-throughput technologies, principal component analysis (PCA) in the high-dimensional regime is of great interest. Most of the existing theoretical and methodological results for high-dimensional PCA are based on the spiked population model in which all the population eigenvalues are equal ex…
Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…
Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific …
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinde…
We examine the effects of instantiating Lewis signaling games within a population of speaker and listener agents with the aim of producing a set of general and robust representations of unstructured pixel data. Preliminary experiments suggest that the set of representations associated with languages generated within a …
Reproductive success and survival are influenced by wealth in human populations. Wealth is transmitted to offsprings and strategies of transmission vary over time and among populations, the main variation being how equally wealth is transmitted to children. Here we propose a model where we simulate both the dynamics of…
Paper tackles causal effect identification in sub-population with latent variables.
New method learns population dynamics from snapshots, outperforming existing models.
Estimates KL divergence with fairness considerations for sub-populations.
Measures of wealth and production have been found to scale superlinearly with the population of a city. Therefore, it makes economic sense for humans to congregate together in dense settlements. A recent model of population dynamics showed that population growth can become superexponential due to the superlinear scalin…