Uniform Inference and Certified Capacity at a Reflexive Stability Boundary

📅 2026-09-02
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🤖 AI Summary
本文通过联合估计条件风险、临时交叉影响和有效风险承受能力,开发了一种在反射稳定性边界上进行统一推理和认证容量决策的方法。
📝 Abstract
This paper develops uniform inference and certified capacity decisions for an estimated financial stability boundary. Conditional risk, temporary cross-impact, and effective risk-bearing capacity are jointly estimated from dependent observations. Conventional pointwise inference is reliable at a separated simple spectral root but can fail near semisimple or defective collisions. Projecting a valid joint confidence region for the underlying inputs avoids this local approximation and yields a three-way regime decision with abstention and a one-sided capacity bound. A verified two-dimensional implementation keeps numerical error from creating a resolved sign. Structural simulations recover the predicted tradeoff between coverage and resolution, while observed-risk stresses distinguish statistical abstention from an insufficient computational budget. The financial conclusions remain conditional on the identification of cross-impact, the normalization of capacity, and stability of the inputs over the action horizon.
Problem

Research questions and friction points this paper is trying to address.

uniform inference
certified capacity
financial stability boundary
conditional risk
cross-impact
Innovation

Methods, ideas, or system contributions that make the work stand out.

uniform inference
certified capacity
financial stability boundary
joint estimation
confidence region
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Alejandro Rodríguez Domínguez
Quantitative Analysis and Artificial Intelligence Department, Miralta Finance Bank S.A., Madrid, Spain; Department of Computer Science, University of Reading, Reading, United Kingdom; Department of Data and AI, Albert School, Paris, France