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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.

168,657 papers · 148 categories

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8 results for “RQMC”

Randomized Quasi-Monte Carlo Features for Kernel Approximationstat.ME

RQMC improves kernel-based learning by reducing deterministic error and offering computational advantages.

problem Improving kernel-based learning methods to reduce deterministic error and computational complexity.

method Randomized quasi-Monte Carlo (RQMC) methods applied to random feature approximations.

result RQMC methods improve deterministic approximation error bound from

O_P(1/\sqrt{M})

O(1/M)

, matching QMC methods.

kernel approximation randomized quasi-Monte Carlo random features Monte Carlo statistical error

2025-03-08abs ↗pdf ↗

Single- and Multi-Level Fourier-RQMC Methods for Multivariate Shortfall Riskq-fin.CP

New methods estimate multivariate shortfall risk more efficiently.

problem Estimating multivariate shortfall risk is computationally challenging.

method Combines Fourier inversion and RQMC sampling in frequency domain.

result Fourier RQMC methods outperform existing benchmarks.

shortfall risk Monte Carlo methods Fourier analysis RQMC sampling financial systems

2026-02-06abs ↗pdf ↗

Quasi-Monte Carlo with Domain Transformation for Efficient Fourier Pricing of Multi-Asset Optionsq-fin.CP

This work improves Fourier pricing for multi-asset options using RQMC with domain transformation.

problem Efficiently pricing multi-asset options in high dimensions with Fourier methods.

method Randomized quasi-Monte Carlo (RQMC) with domain transformation to handle singularities.

result RQMC with domain transformation provides accurate and scalable Fourier pricing for multi-asset options.

Fourier pricing Monte Carlo methods Quasi-Monte Carlo Domain transformation Multi-asset options

2024-03-05abs ↗pdf ↗

Quasi-Newton Quasi-Monte Carlo for variational Bayescs.LG

RQMC improves optimization in variational Bayes problems.

problem Optimizing variational Bayes problems with noisy objective functions.

method Use of randomized quasi-Monte Carlo (RQMC) sampling with stochastic L-BFGS.

result RQMC can significantly speed up optimization and find better parameter values.

variational Bayes Monte Carlo sampling quasi-Monte Carlo stochastic L-BFGS optimization

2021-04-07abs ↗pdf ↗

The importance of being scrambled: supercharged Quasi Monte Carloq-fin.CP

RQMC improves QMC by providing practical error bounds for financial applications.

problem Lack of practical error estimates in QMC methods.

method Combines Sobol LDS with randomized scrambling methods.

result RQMC outperforms standard QMC in convergence rates and provides error bounds.

Quasi Monte Carlo Randomized QMC Sobol sequences Asian options Greeks

2022-10-29abs ↗pdf ↗

Median of Means Sampling for the Keister Functionstat.ME

Median-of-means sampling outperforms mean-of-means for large sample sizes in numerical integration.

problem Improving numerical integration accuracy in high dimensions.

method Median-of-means sampling compared to mean-of-means using RQMC methods.

result Median-of-means sampling is superior for large sample sizes, while mean-of-means is better for smaller sample sizes.

numerical integration median-of-means mean-of-means RQMC Keister function

2025-01-13abs ↗pdf ↗

Transport Quasi-Monte Carlomath.NA

Improves QMC for complex distributions using transport maps.

problem Challenges in applying QMC to general target distributions.

method Train a transport map to approximate target distributions, ensuring RQMC achieves superior error rates.

result Transport QMC achieves faster convergence rates than standard Monte Carlo under mild conditions.

Quasi-Monte Carlo Transport Maps Bayesian Inference Regularization Error Rates

2024-12-21abs ↗pdf ↗

Comparative Study of Monte Carlo and Quasi-Monte Carlo Techniques for Enhanced Derivative Pricingq-fin.PR

This study compares MC and QMC methods for derivative pricing, showing QMC's superior convergence rates.

problem Improving derivative pricing accuracy and efficiency in high-dimensional settings.

method Compared Monte Carlo and quasi-Monte Carlo techniques, focusing on convergence rates and low-discrepancy sequences.

result Quasi-Monte Carlo methods achieve superior convergence rates and reduce root mean square error in derivative pricing.

Monte Carlo Quasi-Monte Carlo Derivative Pricing Convergence Rates Low-Discrepancy Sequences

2025-02-24abs ↗pdf ↗