Poster - Functional Stable Limit in Random Connection Hypergraphs
Aug 4, 2025
·
0 min read
Abstract
We investigate a dynamic random connection hypergraph model based on a bipartite connection structure, in which nodes and hyperedges are modeled by two independent marked Poisson point processes. Nodes are equipped with birth-death dynamics, while hyperedges are temporally localized. Then, edges are formed under spatial and temporal constraints influenced by the vertex marks. In this system, we focus on the edge count process as a function of time within a growing spatial observation window. Under suitable assumptions, we show a functional stable limit theorem the properly rescaled and centered edge count process converges in distribution to a non-Gaussian, heavy-tailed limit in the Skorokhod space.
Date
Aug 4, 2025 4:00 PM — 5:15 PM
Event
Location
Aarhus University - Aarhus Institute of Advanced Studies
6B Hoegh-Guldbergs Gade, Aarhus C, 8000
Authors
Quantitative Researcher
Quantitative Researcher with a PhD in Mathematics, specializing in stochastic modeling, machine learning, and predictive systems for financial markets.
Experienced in probabilistic modeling, Monte Carlo simulation, uncertainty quantification, and statistical validation for data-driven decision-making.
Currently developing intraday energy-market price prediction models and optimal liquidation strategies using machine learning, functional data analysis, and stochastic differential equations.
Interested in market prediction problems where model quality is directly reflected in trading performance and PnL.