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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.

169,236 papers · 148 categories

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1122 · Jun 201819922001200920182026
18 results for Maastricht Treaty

Study shows EU countries have worsening debts and deficits.

problem Worsening public debts and deficits in EU countries.
method Statistical analysis of public debts and deficits between EU and non-EU countries.
result EU countries have worse public debts and deficits than non-EU countries, especially after Euro introduction.

Public debt is one of the important economic variables that quantitatively describes a nation's economy. Because bankruptcy is a risk faced even by institutions as large as governments (e.g. Iceland), national debt should be strictly controlled with respect to national wealth. Also, the problem of eliminating extreme p…

2010-02-12abs ↗pdf ↗

Study identifies vulnerable jurisdictions in tax networks to prevent treaty abuse.

problem Unexpected tax results due to global economic integration.
method Produced weighted graphs, computed centralities, and detected communities based on withholding tax rates.
result Identified jurisdictions likely to be used for treaty shopping and detected community structures.

ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.

problem Opaque and difficult-to-audit reinsurance treaty pricing practices.
method ClauseLens models treaty pricing as a Risk-Aware Constrained Markov Decision Process (RA-CMDP), incorporating legal clauses and generating interpretable explanations.
result ClauseLens reduces solvency violations and improves tail-risk performance, achieving 88.2% accuracy in clause-grounded explanations.

The paper finds socially optimal reinsurance treaties using Value-at-Risk and Range-Value-at-Risk.

problem Finding socially optimal reinsurance treaties in a network of insurance companies.
method Optimization problem with Value-at-Risk and Range-Value-at-Risk, considering monotonicity and stochastic ordering.
result Layer reinsurance treaties are socially optimal under certain conditions.

Mathematical analysis shows Brexit affects EU voting power in unexpected ways.

problem Effects of Brexit on EU voting power and distribution of power.
method Mathematical analysis using Penrose--Banzhaf Index and normal approximation.
result Non-monotonic effects of Brexit on EU voting power, exacerbated by EU population vector.

Optimal reinsurance contracts for multiple dependent risks are derived without specific dependency assumptions.

problem Finding optimal reinsurance contracts for multiple dependent risks without assuming their dependency structure.
method Assumes maximal expected utility criterion and independent negotiation of reinsurance for each risk. Derives optimality conditions and shows that under mild assumptions, optimal contracts are classical (non-randomized) type.
result Optimal reinsurance contracts exist and can be classical (non-randomized) type under mild assumptions.

Deep neural networks classify T2D from retinal images with high accuracy.

problem Detecting early-stage Type 2 Diabetes from retinal images.
method Employed deep neural networks and multi-target learning to differentiate T2D from healthy individuals.
result Classification performance improved to AUC = 0.758 [±\pm0.003] using images from both eyes.

Dynamic reinsurance minimizes insurer's cost of capital over time.

problem Minimizing insurer's cost of capital in a dynamic reinsurance setting.
method Dynamic extension of the static optimal reinsurance problem, viewed as a risk-sensitive Markov Decision Process.
result Existence of a stationary Markovian optimal reinsurance policy under an infinite planning horizon.

Hybrid framework optimizes reinsurance using generative models and reinforcement learning.

problem Traditional reinsurance optimization relies on restrictive assumptions and static designs.
method Combines VAEs for joint distribution learning and PPO for dynamic treaty parameter adaptation.
result Hybrid method produces more resilient outcomes with higher surpluses and lower tail risk.

The paper proposes modern computational methods for optimizing reinsurance contracts.

problem Optimizing catastrophe excess-of-loss reinsurance contracts with realistic constraints and risk measures.
method Two approaches: simulated annealing for local search and quantum branch & bound for future potential.
result Quantum branch & bound approach shows potential for future optimization with quantum computers.

Expert-guided model improves seismic compliance monitoring.

problem Classifying seismic data with missingness and expert knowledge.
method Expert-guided class-conditional model with interpretable goodness-of-fit features.
result Interpretable classifier outperforms standard machine learning, especially with small training data.

We develop a class of non-life reserving models using a stable-1/2 random bridge to simulate the accumulation of paid claims, allowing for an essentially arbitrary choice of a priori distribution for the ultimate loss. Taking an information-based approach to the reserving problem, we derive the process of the condition…

2010-05-04abs ↗pdf ↗

Study optimal reinsurance strategies in a game between insurer and two reinsurers.

problem Optimal reinsurance strategies in a competitive market.
method Stochastic game theory, Stackelberg model, Nash game, time-inconsistent control problem, extended Hamilton-Jacobi-Bellman equation.
result Equilibrium reinsurance strategies and premiums found for exponential claim size.