An aspect of optimal regression design for LSMC

DOI10.1515/mcma-2019-2049OpenAlexW2981461463WikidataQ127024763 ScholiaQ127024763MaRDI QIDQ2293277

Zoran S. Nikolic, Christian Weiß

Publication date: 7 February 2020

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

Full work available at URL: https://arxiv.org/abs/1811.08509

zbMATH Keywords

numerical stabilityorthonormal polynomialslow-discrepancy sequencesleast squares Monte CarloSobol sequences

Mathematics Subject Classification ID

Numerical computation of matrix norms, conditioning, scaling (65F35) Irregularities of distribution, discrepancy (11K38)

Related Items (2)

Modeling surrender risk in life insurance: theoretical and experimental insight ⋮ A generalized Faulhaber inequality, improved bracketing covers, and applications to discrepancy

Uses Software

• TOMS659
• sobol.cc

Cites Work

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• Valuation of the early-exercise price for options using simulations and nonparametric regression
• A least-squares Monte Carlo approach to the estimation of enterprise risk
• ON THE CALCULATION OF RISK MEASURES USING LEAST-SQUARES MONTE CARLO
• Nested Simulation in Portfolio Risk Measurement
• Some basic hypergeometric orthogonal polynomials that generalize Jacobi polynomials
• Algorithm 659
• Irregularities of distribution, VII

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