activity
20152026
most citedExploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques

118 citations · 169 across the 13 of their papers we have counts for

collaborators

14 papers

stat.CO2026

Sequential Monte Carlo Sampling of Feasible Regions Represented by R-Functions

Sinan Yıldırım, Sergei Kucherenko

Feasible-region sampling and volume estimation become increasingly inefficient when constraints define only a small fraction of a convenient bounding domain. We combine R-functions…

cs.LG2026

Geometry-Aware R-Structured Kolmogorov-Arnold Networks

Sergei Kucherenko, Nilay Shah

We propose a novel hybrid neural architecture, the Geometry-aware R-Structured Kolmogorov-Arnold Network (GRS-KAN), which integrates V.L.Rvachev's R-functions into the Kolmogorov-A…

q-fin.CP2026★ 2 cited

Application of Quasi Monte Carlo and Global Sensitivity Analysis to Option Pricing and Greeks

Stefano Scoleri, Marco Bianchetti, Sergei Kucherenko

Quasi Monte Carlo (QMC) and Global Sensitivity Analysis (GSA) techniques are applied for pricing and hedging representative financial instruments of increasing complexity. We compa…

math.NA2026

Active subspace methods and derivative-based Shapley effects for functions with non-independent variables

Matieyendou Lamboni, Sergei Kucherenko

Lower-dimensional subspaces that impact estimates of uncertainty are often described by Linear combinations of input variables, leading to active variables. This paper extends the…

q-fin.CP2025

Effective dimensionality reduction for Greeks computation using Randomized QMC

Luca Albieri, Sergei Kucherenko, Stefano Scoleri +1

Global sensitivity analysis is employed to evaluate the effective dimension reduction achieved through Chebyshev interpolation and the conditional pathwise method for Greek estimat…

q-fin.CP2022

The importance of being scrambled: supercharged Quasi Monte Carlo

J. Hok, S. Kucherenko

In many financial applications Quasi Monte Carlo (QMC) based on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing faster and more stable convergence. However, u…

q-fin.CP2022★ 1 cited

Quasi-Monte Carlo methods for calculating derivatives sensitivities on the GPU

Paul Bilokon, Sergei Kucherenko, Casey Williams

The calculation of option Greeks is vital for risk management. Traditional pathwise and finite-difference methods work poorly for higher-order Greeks and options with discontinuous…

q-fin.CP2021

Pricing and Risk Analysis in Hyperbolic Local Volatility Model with Quasi Monte Carlo

Julien Hok, Sergei Kucherenko

Local volatility models usually capture the surface of implied volatilities more accurately than other approaches, such as stochastic volatility models. We present the results of a…

stat.AP2016★ 2 cited

Quantile based global sensitivity measures

Sergei Kucherenko, Shufang Song

New global sensitivity measures based on quantiles of the output are introduced. Such measures can be used for global sensitivity analysis of problems in which quantiles are explic…

math.NA2016★ 2 cited

Different numerical estimators for main effect global sensitivity indices

Sergei Kucherenko, Shufang Song

The variance-based method of global sensitivity indices based on Sobol sensitivity indices became very popular among practitioners due to its easiness of interpretation. For comple…

math.ST2016★ 40 cited

Derivative-based global sensitivity measures and their link with Sobol sensitivity indices

S. Kucherenko, S. Song

The variance-based method of Sobol sensitivity indices is very popular among practitioners due to its efficiency and easiness of interpretation. However, for high-dimensional model…

math.ST2016★ 2 cited

Sobol' indices for problems defined in non-rectangular domains

S. Kucherenko, O. V. Klymenko, N. Shah

A novel theoretical and numerical framework for the estimation of Sobol sensitivity indices for models in which inputs are confined to a non-rectangular domain (e.g., in presence o…

stat.AP2015★ 118 cited

Exploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques

Sergei Kucherenko, Daniel Albrecht, Andrea Saltelli

Three sampling methods are compared for efficiency on a number of test problems of various complexity for which analytic quadratures are available. The methods compared are Monte C…

q-fin.RM2015★ 2 cited

Pricing and Risk Management with High-Dimensional Quasi Monte Carlo and Global Sensitivity Analysis

Marco Bianchetti, Sergei Kucherenko, Stefano Scoleri

We review and apply Quasi Monte Carlo (QMC) and Global Sensitivity Analysis (GSA) techniques to pricing and risk management (greeks) of representative financial instruments of incr…