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arXiv research

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.

169,341 papers · 148 categories

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159318477636 · Jun 202019922001200920182026
48 results for value process

Investigates set-valued risk measures for processes and vectors, proving equivalence and providing new dual representations.

problem Investigates set-valued risk measures for processes and vectors.
method Utilizes equivalence of risk measures for processes and vectors and their penalty function formulations.
result Provides new dual representation for risk measures for processes in the set-valued framework.

Complex-valued Gaussian process improves time series analysis of brain oscillations.

problem Improving time series analysis of complex-valued signals, especially brain oscillations.
method Modeling real-valued signals as the real part of a latent complex-valued Gaussian process with new covariance functions.
result Complex-valued Gaussian process provides better estimates of amplitude and frequency than existing methods.

Study improves self-normalized bounds for vector-valued processes beyond sub-Gaussianity.

problem Limited understanding of self-normalized concentration for vector-valued processes outside sub-Gaussian frameworks.
method Developed concentration inequalities for self-normalized processes with light tails (e.g., Bennett, Bernstein bounds) for vector-valued data.
result Provided new insights and bounds for self-normalized processes with non-sub-Gaussian distributions.

The paper defines and analyzes set-valued stochastic integrals for Lévy processes.

problem Defining and analyzing set-valued stochastic integrals for Lévy processes.
method Extending classical definitions to convoluted integrals with square-integrable kernels, and proving properties of set-valued convoluted stochastic integrals.
result Set-valued convoluted stochastic integrals can be explosive and take extended vector values.

The paper finds optimal threshold strategies for insurance companies with a positive terminal value at creeping ruin.

problem Optimizing dividend payments in an insurance company's surplus process with a positive terminal value at creeping ruin.
method Using fluctuation theory, the paper derives explicit formulas for the objective function and shows the optimality of threshold strategies.
result Threshold strategies are optimal for the dividend optimization problem under certain conditions.

Algorithm simulates complex-valued Gaussian processes efficiently.

problem Simulating noncircular or improper complex-valued stationary Gaussian processes.
method Circulant embedding method for multivariate Gaussian processes.
result Exact simulation possible except for negative eigenvalues.

Framework for energy markets using measure-valued processes.

problem Arbitrage-free modeling of energy futures markets.
method Translation of Heath-Jarrow-Morton approach to measure-valued processes, derivation of HJM-drift condition, analysis of measure-valued diffusions.
result Existence of non-negative measure-valued diffusions satisfying the HJM-drift condition.

Optimizes dividend payout in insurance wealth process with stochastic interest rate.

problem Maximizing expected discounted dividends up to ruin in insurance wealth process.
method Modelled compound Poisson process with stochastic interest rate, solved using HJB equation.
result Explicit expression for value function and optimal strategy in geometric Brownian motion case.

Study on network-valued processes with asynchronous updates, proving consistency in community and changepoint estimation.

problem Understanding the behavior of network-valued stochastic processes with asynchronous updates.
method Analysis of concentration properties of aggregated adjacency and Laplacian matrices for lazy network-valued stochastic processes.
result Demonstrates consistency of estimators in community and changepoint estimation problems.

Develops intrinsic Gaussian process regression for manifold-valued data.

problem Lack of intrinsic Gaussian process methods for manifold-valued response variables.
method Proposes an intrinsic covariance structure and a novel intrinsic Gaussian process regression model.
result Establishes asymptotic properties and shows posterior consistency.

The paper shows robustness of Hilbert space-valued stochastic volatility models to perturbations.

problem Robustness of Hilbert space-valued stochastic volatility models to measurement or approximation errors.
method Quantifying the error induced by volatility perturbations and studying robustness of volatility process with finite dimensional approximations.
result Explicit bounds for the induced error in terms of approximation of the underlying parameter.

A stochastic model helps maintain insufficiently funded pension funds.

problem Maintaining pension funds that are underfunded and require external financing.
method A time-homogeneous diffusion process with a barrier is used to model the unrestricted reserves value, and a renewal-reward process models the financing effort.
result Expected values and cost evaluations of maintenance are derived, and the approach is applied to a generalized Brownian motion process.

Improves BO methods for categorical and integer-valued variables.

problem Handling categorical and integer-valued variables in BO with GPs.
method Proposes a principled approach to encode categorical and integer-valued inputs.
result Significantly improves BO results on problems with categorical or integer-valued variables.

We introduce a new method to explain Gaussian processes using Shapley values.

problem Explaining the uncertainty in Gaussian process models.
method Extending Shapley values to stochastic cooperative games for Gaussian processes.
result Our method generates explanations that are random variables and satisfy favorable axioms.

The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.

problem Estimating excursion sets of vector-valued Gaussian processes.
method Clarifying the connection between continuous Gaussian processes and Gaussian measures in Banach spaces, extending concepts and properties from scalar-valued settings to vector-valued settings.
result Consistency results for sequential design strategies can be applied to vector-valued Gaussian processes.

A new method for BO with GPs handles integer-valued inputs.

problem Optimizing functions with integer-valued variables using Gaussian processes.
method A principled approach to handle integer-valued inputs without rounding.
result Significant improvement in BO results for problems with integer-valued variables.

Gaussian processes adapted for Riemannian manifolds using gauge-independent kernels.

problem Deploying Gaussian processes on non-Euclidean domains like Riemannian manifolds.
method Developed techniques to generalize Gaussian processes to vector fields on Riemannian manifolds using gauge-independent kernels.
result Enabled training of vector-valued Gaussian processes on Riemannian manifolds using standard Gaussian process methods.

Paper develops methods for estimating and forecasting integer-valued trawl processes.

problem Estimation and forecasting of continuous-time integer-valued trawl processes.
method Composite likelihood methods, focusing on pairwise likelihood.
result Consistency and asymptotic normality of the estimator in the short memory case.

Develops methods to find most probable paths on complex manifolds.

problem Identifying optimal paths for manifold-valued processes, especially those with non-trivial structures.
method Constructs a general approach to defining and identifying most probable paths by measuring the Onsager-Machlup function on the anti-development of such processes.
result Derives explicit equations for development most probable paths that encompass various manifold-valued processes.

The generic identification problem is to decide whether a stochastic process (Xt)(X_t) is a hidden Markov process and if yes to infer its parameters for all but a subset of parametrizations that form a lower-dimensional subvariety in parameter space. Partial answers so far available depend on extra assumptions on the pro…

2011-01-19abs ↗pdf ↗

A new method uses deep Gaussian processes to handle missing values in irregularly sampled healthcare data.

problem Missing values and irregular sampling in healthcare data.
method Deep Gaussian process emulation with stochastic imputation.
result The method outperforms conventional imputation methods in clinical datasets.

Proposes random Euler filters for efficient complex-valued nonlinear signal processing.

problem Efficiently processing complex-valued nonlinear signals with reduced computational cost.
method Introduces linear and widely-linear random Euler complex-valued filters with fixed network structures.
result Analytical minimum mean square error and optimum step-size derived for transient and steady-state performances.

NP-PROV separates mean and variance spaces to improve function uncertainty.

problem Neural Processes fail on out-of-domain tasks due to shared latent space uncertainty.
method Separates mean and variance into function-value-related and position-related latent spaces.
result NP-PROV achieves state-of-the-art likelihood with bounded variance in drifts.

Adaptive importance sampling for estimating point process statistics.

problem Estimating the expected value of a statistic of a locally stable point process.
method Adaptive importance sampling with Poisson point processes and cross-entropy minimization.
result The proposed estimator converges to the target value almost surely and is asymptotically normal.

The paper constructs Markov processes for stochastic heat equations on infinite strings with manifold values.

problem Constructing Markov processes for stochastic heat equations on infinite strings with manifold values.
method Constructing conservative Markov processes corresponding to martingale solutions to stochastic heat equations on R+\mathbb{R}^+ or R\mathbb{R} with values in a Riemannian manifold.
result The process exhibits exponential ergodicity if the Ricci curvature is strictly positive and non-ergodicity if the sectional curvature is negative.

Study uses viscosity solutions to solve control problems involving measure-valued martingales.

problem Stochastic control problems with measure-valued martingale state processes.
method Viscosity solution approach exploiting structural properties of MVM processes.
result Value function is the unique viscosity solution to the HJB equation.

A new STAR framework models integer-valued data with flexible distributions.

problem Modeling integer-valued data with flexibility and accuracy.
method Simultaneously Transforming and Rounding (STAR) a continuous-valued process.
result STAR framework designs a new BART model for integer-valued data with impressive predictive accuracy.

We propose a model for the credit markets in which the random default times of bonds are assumed to be given as functions of one or more independent "market factors". Market participants are assumed to have partial information about each of the market factors, represented by the values of a set of market factor informa…

2010-06-15abs ↗pdf ↗

This research adapts superpixels for Shapley value computation in DNA profile classification.

problem Efficiently computing Shapley values for large, multidimensional time-series data.
method Adapting the concept of superpixels to streamline Shapley value computation for time-series-like data.
result Realistic, accurate, and fast computation of Shapley values for DNA profile classification.

A privacy-preserving framework detects faults in circular economy processes.

problem Lack of shared data across company borders due to privacy concerns.
method Federated Principal Component Analysis (PCA) and Secure Multiparty Computation.
result The proposed FedMSPC framework outperforms standard PCA in fault detection.

A new estimator for state values in reinforcement learning reduces complexity and improves convergence.

problem Estimating state values in reinforcement learning with Markov reward processes.
method Loop estimator exploiting regenerative structure of Markov reward processes.
result Instance-dependent convergence rate of O~(τs/T)\widetilde{O}\left(\sqrt{τ_s/T}\right) for estimating state values.

We characterize value functions in partially observable MDPs as semi-algebraic sets.

problem Understanding feasible value functions in partially observable Markov decision processes.
method Characterization of feasible value functions as semi-algebraic sets defined by polynomial inequalities.
result The feasible set of value functions in POMDPs is a semi-algebraic set, not a polytope as in MDPs.

We study time-consistency questions for processes of monetary risk measures that depend on bounded discrete-time processes describing the evolution of financial values. The time horizon can be finite or infinite. We call a process of monetary risk measures time-consistent if it assigns to a process of financial values …

2004-10-21abs ↗pdf ↗

Extends XVA valuation under stochastic volatility, characterizing value processes via mild solutions.

problem Valuation of contingent claims in presence of default, collateral, and funding under stochastic volatility.
method Characterizes pre-default value processes via mild solutions to parabolic semilinear PDEs under stochastic volatility.
result Characterizes pre-default value processes via mild solutions to parabolic semilinear PDEs under stochastic volatility, providing sufficient conditions for existence and uniqueness.