自反稳定边界下的统一推断与认证容量决策
Uniform Inference and Certified Capacity at a Reflexive Stability Boundary
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中文总结 AI 辅助
本文针对金融稳定边界，提出统一推断与认证容量决策方法，通过联合估计相关风险等，避免局部近似，区分统计弃权与计算不足，验证了相关权衡关系。
中文摘要 AI 辅助
本文针对估计的金融稳定边界，提出了统一推断与认证容量决策方法。从相依观测中联合估计条件风险、临时交叉影响及有效风险承担能力。传统逐点推断在分离的简单谱根处可靠，但在半单或缺陷碰撞附近可能失效。对基础输入的有效联合置信区域进行投影，可避免该局部近似，得到包含弃权（不执行）与单侧容量界的三方状态决策。经验证的二维实现可防止数值误差产生已解析符号。结构模拟恢复了覆盖度与分辨率之间的预测权衡，而观测风险压力可区分统计弃权（不执行）与计算预算不足。金融结论仍以交叉影响的识别、容量的归一化及行动范围内输入的稳定性为条件。
英文摘要
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.
发表机构
- Miralta Finance Bank S.A.（米尔塔特银行股份有限公司）
- University of Reading（雷丁大学）
- Albert School（阿尔伯特学院）
机构由 AI 辅助整理，请以论文原文为准。
