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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.

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48 results for Poisson counter

A new SNN model explains decision-making with learning and spiking neurons.

problem Lack of learning mechanism in existing models for decision-making.
method Proposes a Spiking Neural Network (SNN) model that incorporates a learning mechanism and uses multivariate Hawkes processes.
result Shows a coupling between DDM and Poisson counter models and derives a DDM from a Hawkes network of spiking neurons.

Study on pairwise counter-monotonicity, a type of negative dependence.

problem Understanding and quantifying extremal negative dependence structures.
method Established stochastic representation and invariance property; showed implications and connections.
result Pairwise counter-monotonicity implies negative association and joint mix dependence.

In [1] Zawadoski introduces a banking network model in which the asset and counter-party risks are treated separately and the banks hedge their assets risks by appropriate OTC contracts. In his model, each bank has only two counter-party neighbors, a bank fails due to the counter-party risk only if at least one of its …

2014-02-21abs ↗pdf ↗

This paper shows how to calculate risk measures for sums of two counter-monotonic risks.

problem Calculating risk measures for sums of two counter-monotonic risks.
method Using a fixed distortion function and expressing the risk measure of a sum as the sum of two related measures of the marginals.
result The risk measure of a sum of two counter-monotonic risks can be expressed as the sum of two related distortion risk measures of the marginals.

Over-the-counter markets are at the center of the postcrisis global reform of the financial system. We show how the size and structure of such markets can undergo rapid and extensive changes when participants engage in portfolio compression, a post-trade netting technology. Tightly-knit and concentrated trading structu…

2017-05-19abs ↗pdf ↗

Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a m…

2018-04-11abs ↗pdf ↗

The paper studies risk-sharing allocations for risk-seeking agents using a common distortion risk measure.

problem Characterizing Pareto-optimal risk-sharing allocations for risk-seeking agents.
method Modeling preferences with a common distortion risk measure and analyzing three settings: risk-averse, risk-seeking, and inverse S-shaped distortion.
result Pareto-optimal allocations for risk-seeking agents are counter-monotonic, not comonotonic.

We review the Burghelea conjecture, which constitutes a full computation of the periodic cyclic homology of complex group rings, and its relation to the algebraic Baum-Connes conjecture. The Burghelea conjecture implies the Bass conjecture. We state two conjectures about groups of finite asymptotic dimension, which tog…

2016-10-31abs ↗pdf ↗

Study risk sharing among agents with varying risk preferences.

problem Risk sharing among agents with heterogeneous risk measures.
method Derive explicit solutions for inf-convolution and counter-monotonic inf-convolution under varying risk seeking.
result Explicit solutions for inf-convolution and counter-monotonic inf-convolution can be represented by a generalization of distortion risk measures.

A new method for estimating adversarial strategies in nonlinear systems.

problem Inferring an intelligent adversarial agent's strategy in highly nonlinear systems.
method Formulated inverse cognition as a nonlinear Gaussian state-space model and developed an inverse UKF (IUKF) system.
result The estimation error of IUKF converges and closely follows the recursive Cramér-Rao lower bound.

We study two classes of over-the-counter markets specified by systems of ODE's, in the spirit of Duffie-Garleanu-Pedersen, Econometrica, 2005. We first compute the steady states for many of these ODE's. Then we obtain the prices at which investors trade with each other at these steady states. Finally, we study the stab…

2013-08-13abs ↗pdf ↗

In this paper, we have studied the pricing of a continuously collateralized CDS. We have made use of the "survival measure" to derive the pricing formula in a straightforward way. As a result, we have found that there exists irremovable trace of the counter party as well as the investor in the price of CDS through thei…

2011-04-11abs ↗pdf ↗

Paper uses reinforcement learning to optimize bid-ask spreads in OTC markets.

problem Optimizing bid-ask spreads in over-the-counter markets with dynamic order sizes.
method Reinforcement learning to solve high-dimensional stochastic control problem.
result Optimal bid-ask spreads follow a Gaussian distribution under certain conditions.

Forward-Euler fails for simulating Wasserstein gradient flows with KL divergence.

problem Simulating Wasserstein gradient flows with forward-Euler discretization fails for KL divergence.
method Forward-Euler discretization for Wasserstein gradient flows with KL divergence.
result Forward-Euler discretization can be incorrect for Wasserstein gradient flows with KL divergence.

The paper disproves a generalized toral rank conjecture with various counter-examples.

problem The conjecture that the sum of Betti numbers of a compact manifold with a torus action is bounded by 2r2^r.
method Provided counter-examples of smooth nilpotent fibre bundles of nilmanifolds with torus fibres of rank rr.
result There are sequences of torus fibrations with total space cohomology dimensions converging to 0 as rank rr increases.

Cincer cleans both new and past data by identifying and relabeling suspicious and counter-examples.

problem Sequential learning under label noise, especially in applications with human supervision.
method Cincer uses example-based explanations to identify and relabel suspicious and counter-examples, leveraging Fisher information matrix approximation.
result Cincer achieves better data and models by clarifying the model's suspicions, especially with FIM approximation.

Geometrically proves twisted Poincaré duality for orientable Poisson manifolds.

problem Establishing twisted Poincaré duality for Poisson manifolds.
method Geometrically reinterprets algebraic constructions of twisted Poisson modules and Poisson chain complexes.
result Explicit chain isomorphism between Poisson cochain and chain complexes with coefficients in Poisson modules.

We propose a Poisson-Lie analog of the symplectic induction procedure, using an appropriate Poisson generalization of the reduction of symplectic manifolds with symmetry. Having as basic tools the equivariant momentum maps of Poisson actions, the double group of a Poisson-Lie group and the reduction of Poisson manifold…

2000-12-11abs ↗pdf ↗

New Poisson structures on algebras linked to derivatives.

problem Understanding Poisson structures on trivial extension algebras.
method Introducing a correspondence between Poisson structures and derivatives, and formulating results in terms of Lie algebroids.
result One-to-one correspondence between Poisson structures and data involving derivatives.

Optimal risk sharing found for heterogeneous risk attitudes using distortion risk measures.

problem Risk sharing in economies with diverse risk attitudes.
method Modeling preferences with distortion risk measures, using comonotonic and counter-monotonic principles.
result Optimal risk sharing strategies identified based on risk attitudes, reducing the nn-agent problem to a two-agent formulation.

We semiclassicalise the theory of quantum group principal bundles to the level of Poisson geometry. The total space XX is a Poisson manifold with Poisson-compatible contravariant connection, the fibre is a Poisson-Lie group in the sense of Drinfeld with bicovariant Poisson-compatible contravariant connection, and the …

2019-03-28abs ↗pdf ↗

We construct a Poisson isomorphism between the formal Poisson manifolds g^* and G^*, where g is a finite dimensional quasitriangular Lie bialgebra. Here g^* is equipped with its Lie-Poisson (or Kostant-Kirillov-Souriau) structure, and G^* with its Poisson-Lie structure. We also quantize Poisson-Lie dynamical r-matrices…

2004-12-17abs ↗pdf ↗

The paper establishes a Poisson Poincaré-Dulac theorem for Poisson-flat connections.

problem Analyzing Poisson-flat connections with logarithmic poles.
method Defining an Euler-Poisson principal part and residue theory, establishing a Poisson Poincaré-Dulac theorem.
result Any logarithmic Poisson-flat connection is holomorphically gauge equivalent to a pure Euler-Poisson normal form.

The first cohomology of Poisson algebras is described and conditions for its vanishing are established.

problem Understanding the first cohomology of Poisson algebras.
method Description and mapping of first cohomology to intrinsic cohomologies of Poisson submanifolds, formulation of vanishing conditions.
result Necessary and sufficient conditions for the vanishing of the first cohomology of infinitesimal Poisson algebras are derived.

A new Poisson bracket defined on Poisson structures with applications to fixed points and cohomology.

problem Defining a Poisson bracket on the space of Poisson structures.
method Constructing a Poisson bracket on P(M)\mathcal{P}(M) depending on a volume form, and defining invariant of Poisson structures.
result Invariant of Poisson structures detects unimodularity and related Poisson bracket for symplectic structures.

This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known Lévy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson proc…

2011-03-03abs ↗pdf ↗

TRASHFIRE improves model robustness by analyzing training rates and costs.

problem Understanding and predicting model robustness under adversarial conditions.
method Survival models, worst-case examples, cost-aware analysis.
result Deeper models offer marginal robustness gains due to inference time, not inherent robustness.