Implementing de-biased estimators using mixed sequences

DOI10.1515/mcma-2020-2075zbMath1469.65016OpenAlexW3092214866MaRDI QIDQ2026640

Arun Kumar Polala, Giray Ökten

Publication date: 20 May 2021

Published in: Monte Carlo Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/mcma-2020-2075

zbMATH Keywords

low-discrepancy sequencesde-biased estimatormixed sequences

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Numerical solutions to stochastic differential and integral equations (65C30)

Uses Software

• MersenneTwister
• TOMS659
• sobol.cc

Cites Work

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• Randomized quasi-Monte Carlo methods in pricing securities
• On the \(L_2\)-discrepancy for anchored boxes
• Efficient Monte Carlo simulation of security prices
• A central limit theorem and improved error bounds for a hybrid-Monte Carlo sequence with applications in computational finance
• Unbiased Estimation with Square Root Convergence for SDE Models
• A Theory of the Term Structure of Interest Rates
• A Probabilistic Result on the Discrepancy of a Hybrid-Monte Carlo Sequence and Applications
• Correction of a proof in “A probabilistic result on the discrepancy of a hybrid-Monte Carlo sequence and applications”
• Algorithm 659
• Uniformly distributed sequences with an additional uniform property
• Mersenne twister
• A general method for debiasing a Monte Carlo estimator
• Remark on algorithm 659