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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.

168,695 papers · 148 categories

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48 results for Consistency Rates

The paper introduces risk consistency properties for credit ratings.

problem Promoting prudent investment decisions in credit ratings.
method Introducing and studying risk consistency properties in the framework of Choquet rating criteria.
result Characterization of Choquet risk measures and rating criteria satisfying risk consistency properties.

Prototype rules simplify multiclass classification in metric spaces, achieving consistency and reduced complexity.

problem Multiclass classification in metric spaces, focusing on universal consistency and convergence rates.
method Novel Proto-NN and hybrid rules for multiclass classification in metric spaces, analyzing convergence rates.
result Proto-NN is universally consistent and simpler to implement, with similar computational advantages.

Develops a framework for consistent pricing of interest rate derivatives.

problem Consistent pricing of bivariate interest rate exotics across interconnected markets.
method Schrödinger optimal transport problem with constraints.
result Demonstrates practical applicability and no-arbitrage bounds computation.

New theorem for generalized group sparsity improves consistency and convergence rates.

problem Improving statistical inference in high-dimensional data with element-wise and group-wise sparsity.
method Developed a generalized version of Sparse-Group Lasso and proved a universal theorem for consistency and convergence rates.
result Obtained results on consistency and convergence rates for different forms of double sparsity regularization.

The paper studies multi-curve interest rate models and their consistency and finite-dimensional realizations.

problem Consistency and existence of finite-dimensional realizations for multi-curve interest rate models.
method Geometric approach, characterizing consistency and existence of finite-dimensional realizations for multi-curve models.
result Characterization of consistency and existence of finite-dimensional realizations for multi-curve models.

Deep neural networks without regularization can achieve consistent estimates with good convergence rates.

problem The necessity of regularization in deep neural networks for consistent estimates.
method Gradient descent on an over-parametrized neural network without regularization, with specific initialization, step size, and number of steps.
result An estimate without regularization is universally consistent and achieves good convergence rates.

We derive high-probability finite-sample uniform rates of consistency for kk-NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that kk-NN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the kk-NN regression rates to establish new …

2017-07-19abs ↗pdf ↗

Research on nearest-neighbor methods tends to focus somewhat dichotomously either on the statistical or the computational aspects -- either on, say, Bayes consistency and rates of convergence or on techniques for speeding up the proximity search. This paper aims at bridging these realms: to reap the advantages of fast …

2019-10-07abs ↗pdf ↗

We establish the consistency of an algorithm of Mondrian Forests, a randomized classification algorithm that can be implemented online. First, we amend the original Mondrian Forest algorithm, that considers a fixed lifetime parameter. Indeed, the fact that this parameter is fixed hinders the statistical consistency of …

2017-11-08abs ↗pdf ↗

Gaussian OBFS proves strong consistency in feature selection with correlations.

problem Feature selection consistency in the presence of correlations.
method Proves strong consistency of Gaussian OBFS under mild conditions.
result Identifies selected features and rates of convergence for different feature types.

Study optimal stopping for group with diverse discount rates using an attitude function.

problem Optimal stopping for a group with diverse discount rates under an aggregation preference.
method Develop iterative approach using consistent planning for time-consistent equilibria.
result Characterize all time-consistent mild equilibria as fixed points of an operator.

This article studies the achievable guarantees on the error rates of certain learning algorithms, with particular focus on refining logarithmic factors. Many of the results are based on a general technique for obtaining bounds on the error rates of sample-consistent classifiers with monotonic error regions, in the real…

2015-12-22abs ↗pdf ↗

The paper proves neural networks' consistency and optimal convergence rates for various function classes.

problem Proving neural networks' consistency and optimal convergence rates for diverse function classes.
method Analyzes wide and deep ReLU neural networks trained on logistic loss and Kolmogorov-Donoho optimal function classes.
result Proves universal consistency and minimax optimal convergence rates for neural networks.

In this paper we study the consistency of an empirical minimum error entropy (MEE) algorithm in a regression setting. We introduce two types of consistency. The error entropy consistency, which requires the error entropy of the learned function to approximate the minimum error entropy, is shown to be always true if the…

2014-12-17abs ↗pdf ↗

Triangular flows ensure statistical consistency and fast rates in generative modeling.

problem Ensuring statistical consistency and fast rates in generative models.
method Statistical guarantees and sample complexity bounds for triangular flow models using empirical process theory.
result Established statistical consistency and finite sample convergence rates for Kullback-Leibler estimator of Knöthe-Rosenblatt measure coupling.

Paper bounds convergence rate of adversarial surrogate risk.

problem Vulnerability of binary classification models to adversarial attacks.
method Characterizes conditions for adversarial consistency and provides surrogate risk bounds.
result Surrogate risk bounds quantify the rate of convergence of adversarial classification risk.

The paper proposes a new method to estimate interest rates consistently under both risk-neutral and real-world measures.

problem Consistent estimation of interest rates under both risk-neutral and real-world measures.
method Proposes a framework using progressive and square-integrable functions to specify the change of measure, and introduces two time-dependent candidates: step and linear functions.
result The proposed methods produce more stable and realistic long-term interest rate forecasts compared to using a constant function.

Study identifies and validates a method for system identification of Markov jump linear systems.

problem System identification for autonomous Markov jump linear systems with complete state observations.
method Proposes switched least squares method for identification and derives rates of convergence.
result Data-independent rate of convergence is O(log(T)/T)\mathcal{O}\big(\sqrt{\log(T)/T} \big), showing strong consistency.

This note fills the gap in market-consistent valuation of lifelong health insurance products.

problem Market-consistent valuation of lifelong health insurance products is not well-addressed.
method Constructs a valuation portfolio to separate Best Estimate into policy data and financial instrument prices.
result The Best Estimate valuation is not uniquely determined by prevailing term structures and requires a stochastic model.

Large learning rates cause oscillations in NN weights that improve generalization.

problem Improving generalization of neural networks trained with large learning rates.
method Theoretical analysis and feature-noise data generation model.
result Oscillating SGD with large learning rates benefits NN generalization by effectively learning weak features.

Paper optimizes federated PCA for covariance estimation under privacy constraints.

problem Privacy-preserving covariance estimation in federated learning.
method Federated PCA, matrix version of van Trees' inequality, three-layer spectral decomposition.
result Optimal rates of convergence for central server's estimation, robust to inconsistent local estimators.

Investigates RI strategies for life insurers with LRD mortality rates.

problem Effect of long-range dependent mortality rates on RI strategies.
method Volterra mortality model, compound Poisson process, open-loop equilibrium mean-variance criterion.
result Explicit equilibrium RI controls derived and uniqueness studied.

Paper establishes a universal growth rate for smooth surrogate losses in classification.

problem Analyzing growth rates of consistency bounds for various surrogate losses.
method Proves square-root growth rate for smooth margin-based losses; extends to multi-class classification.
result Demonstrates a universal square-root growth rate for smooth comp-sum and constrained losses.

Demographic projections of future mortality rates involve a high level of uncertainty and require stochastic mortality models. The current paper investigates forward mortality models driven by a (possibly infinite dimensional) Wiener process and a compensated Poisson random measure. A major innovation of the paper is t…

2019-07-11abs ↗pdf ↗

Classical scaling is shown to be optimal under various noisy conditions.

problem Consistency of classical scaling under general noise conditions.
method Established using finite fourth moments of noise, derived convergence rates, and matching minimax lower bounds.
result Classical scaling achieves minimax optimality in recovering true configuration from noisy dissimilarities.

Paper develops Euler scheme for fractional delay diff. eqs with additive noise.

problem Developing a consistent Euler-Maruyama scheme for fractional stochastic delay diff. eqs.
method Euler-Maruyama scheme for fractional Brownian motion with additive noise.
result Achieved convergence rate of H+1/2 for smooth delays when H>1/2.

Linear-Core Surrogates combine fast optimization and statistical efficiency in classification and structured prediction.

problem The trade-off between smoothness and margin-based losses in classification and structured prediction.
method Linear-Core (LC) Surrogates, a family of convex loss functions that stitch a linear core to a smooth tail.
result LC Surrogates achieve fast linear consistency rates while maintaining differentiability and strict HH-consistency bounds.

Unified model for equity option pricing and interest-rate risk assessment.

problem Pricing short and medium-term equity options and interest-rate risk.
method Developed a stochastic modeling framework using Heston, Bates, and CIR models, calibrated using Fourier inversion and FFT.
result Calibration stability and convergence of parameter sets across models.

Study on HH-consistency bounds for machine learning surrogates.

problem Estimating target loss error relative to surrogate loss error in machine learning.
method Developed HH-consistency bounds for various surrogates and loss functions.
result Stronger guarantees than existing methods, offering distribution-dependent and -independent bounds.

New methods lift weak supervision to structured prediction, providing robustness guarantees.

problem Applying weak supervision techniques to structured prediction problems.
method Introducing pseudo-Euclidean embeddings, tensor decompositions, and invariants for consistent noise rate estimation.
result Generalization guarantees nearly identical to those for models trained on clean data.

In this work we introduce Heath-Jarrow-Morton (HJM) interest rate models driven by fractional Brownian motions. By using support arguments we prove that the resulting model is arbitrage free under proportional transaction costs in the same spirit of Guasoni [Math. Finance 16 (2006) 569-582]. In particular, we obtain a …

2008-02-09abs ↗pdf ↗

Paper compares ETF and futures carry rates in segmented Bitcoin markets.

problem Limitations in cross-margining between spot Bitcoin and CME futures.
method Estimates carry rates from IBIT options and CME futures, uses put-call parity and daily ETF holdings.
result Mean and median wedge in carry rates is 2.58 and 2.52 percent, respectively.

The general problem of asset pricing when the discount rate differs from the rate at which an asset's cash flows accrue is considered. A pricing kernel framework is used to model an economy that is segmented into distinct markets, each identified by a yield curve having its own market, credit and liquidity risk charact…

2018-01-15abs ↗pdf ↗

We study the set G of growth rates of of ideal Coxeter groups in hyperbolic 3-space which consists of real algebraic integers greater than 1. We show that (1) G is unbounded above while it has the minimum, (2) any element of G is a Perron number, and (3) growth rates of of ideal Coxeter groups with nn generators are l…

2015-07-09abs ↗pdf ↗

Study confirms learning rates for vector-valued spectral algorithms, proving consistency.

problem Theoretical confirmation of learning rates for vector-valued spectral algorithms.
method Rigorous analysis of learning rates for various vector-valued spectral algorithms, including kernel ridge regression and gradient descent.
result Upper and lower bounds on learning rates for vector-valued spectral algorithms, proving minimax optimality in various scenarios.