Probability & Statistics · Mathematics & Statistics
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298 papers, from 31 August 2026 to 5 November 2026: 270 arXiv preprints and 28 OpenAlex records.
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Optimal Catastrophe Bond Design and its Applications to Climate Change Risk
Catastrophe bonds have become a central instrument for transferring catastrophic risk to capital markets, yet their payout structures are typically specified exogenously rather than optimized.Global Structure and Local Specifications in Sublinear Valuation
This work studies the relationships among sublinear valuation rules, uncertainty structures, and local specifications in a time-homogeneous Markovian framework with killing.A Tale of Two Walks: Kipnis, Marchioro and Presutti Meet Kac in a Quantum World
We reveal an unexpected connection between the parallel Kac's walk and the Kipnis-Marchioro-Presutti (KMP) process.Achieving an $O(1/N)$ Optimality Gap in Average-Reward Weakly-Coupled MDPs
We study average-reward weakly-coupled Markov decision processes (WCMDPs), where a WCMDP consists of $N$ smaller MDPs, called arms, that share multiple per-step budget constraints.Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions
Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data.A unified interpretation of probability
In this article I revisit the frequency interpretation of probability of von Mises, and argue that it contains the key elements of any coherent interpretation of scientific probability -…Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and Itô Frameworks
Stochastic differential equations (SDEs) can be studied via Itô calculus and rough path theory.Short-term barrier option price expansion
We derive a short-maturity expansion for up-and-out put barrier option prices under continuous stochastic volatility when the strike and the barrier approach the spot at the diffusive scale.Probabilistic Geodesic Flow Matching on Location-Scale Families
Flow matching (FM) has recently emerged as a promising framework for generative modeling due to its conceptual simplicity and strong empirical performance.Structured Neural SDEs for Functional Calibration
Neural Stochastic Differential Equations (Neural SDEs) provide flexible continuous-time generative models, but generic neural drift and diffusion networks are costly to simulate on long horizons and…Optimal Liquidation with Support and Resistance Levels under Multi-Skew Brownian Motion
We solve the perpetual liquidation problem for a geometric multi-skew Brownian motion carrying local-time pushes upward at a support level and downward at a resistance level, a model of technical…Transpose-odd operator chirality in co-moving multiplicative processes: exact tail-level structure and a detectability obstruction
We ask whether transposition changes the stationary heavy tail of the co-moving recursion $\mathbf x_{t+1}=Q_tBQ_t^\top\mathbf x_t+Q_t\boldsymbolη_t$ in $d=3$.Preferent Compression Bounds Are Tight
The lack of rigorous safety and performance certificates remains a key bottleneck to the deployment of modern learning-based methods.When Hedging Changes the Payoff: Option Replication with Price Impact and Execution Costs
Hedging a derivative by trading the underlying asset changes the payoff that the hedging intended to replicate.Distance flexibility in spatial matching: the value of concentration
In spatial matching markets, a supply unit's flexibility is measured by its service radius, the maximum distance at which it can serve demand.Basket implied volatility skew and stickiness
We study the short-maturity implied volatility and the skew stickiness ratio for baskets of assets with continuous, possibly rough, stochastic volatility.Non-Adaptive Learning of Sparse Erdős--Rényi Graphs via Affine Splitting
Graph learning from edge-detecting queries concerns the reconstruction of an unknown edge set on a known vertex set.Affine Geometry of Gaussian ReLU Networks via Conditional Kac-Rice Formulas
We study how the affine geometry of finite ReLU networks is created at random initialization and reorganized by supervised training.Domain Adaptation with Target Information via Doubly-Anchored Distributionally Robust Optimization
Domain Adaptation (DA) often lacks worst-case guarantees, while Distributionally Robust Optimization (DRO) based only on source data centers its ambiguity set at the source law and ignores available…On the asymptotic shape of quantile surfaces
This article is concerned with the asymptotic shape of quantile surfaces, defined as the set of quantiles at a given level $α$ generated by a controlled one-dimensional distribution.