The Importance of Being Scrambled: Supercharged Quasi Monte Carlo

Journal of Risk, Vol 26, Number 1, 2023

19 Pages Posted: 4 Dec 2022 Last revised: 20 Oct 2023

Date Written: October 16, 2023


In many nancial applications Quasi Monte Carlo (QMC) based
on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing
faster and more stable convergence. However, unlike MC QMC lacks a practical
error estimate. Randomized QMC (RQMC) method combines the best
of two methods. Application of scrambled LDS allow to compute con dence
intervals around the estimated value, providing a practical error bound. Randomization
of Sobol' LDS by two methods: Owen's scrambling and digital shift
are compared considering computation of Asian options and Greeks using hyperbolic
local volatility model. RQMC demonstrated the superior performance
over standard QMC showing increased convergence rates and providing practical
error bounds around the estimated values. Eciency of RQMC strongly depend
on the scrambling methods. We recommend using Sobol LDS with Owens
scrambling. Application of e ective dimension reduction techniques such as the
Brownian bridge or PCA is critical to dramatically improve the eciency of
QMC and RQMC methods based on Sobol LDS.

Keywords: Quasi Monte Carlo, Randomized Quasi Monte Carlo, Sobol sequences, Monte Carlo option pricing, Skew hyperbolic local volatility model

Suggested Citation

Hok, Julien and Kucherenko, Sergei, The Importance of Being Scrambled: Supercharged Quasi Monte Carlo (October 16, 2023). Journal of Risk, Vol 26, Number 1, 2023 , Available at SSRN: or

Julien Hok (Contact Author)

Investec Bank ( email )

United Kingdom

Sergei Kucherenko


Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics