An iterative algorithm to determine the number of time steps in path generation methods

DOI10.1002/mma.3632zbMath1338.65005OpenAlexW2398409389MaRDI QIDQ2814080

Chenxi Fan, Qing-Biao Wu

Publication date: 17 June 2016

Published in: Mathematical Methods in the Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/mma.3632

zbMATH Keywords

option pricingMonte Carlo methodsquasi-Monte Carlo methodsapplications to actuarial sciences and financial mathematicsBrownian bridge methodspath generation methods

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Monte Carlo methods (65C05) Numerical integration (65D30)

Cites Work

• Unnamed Item
• The Pricing of Options and Corporate Liabilities
• How do path generation methods affect the accuracy of quasi-Monte Carlo methods for problems in finance?
• Fast orthogonal transforms and generation of Brownian paths
• On the tractability of the Brownian bridge algorithm
• When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?
• The Brownian bridge does not offer a consistent advantage in quasi-Monte Carlo integration
• Smoothness and dimension reduction in quasi-Monte Carlo methods
• New Brownian bridge construction in quasi-Monte Carlo methods for computational finance
• Good Path Generation Methods in Quasi-Monte Carlo for Pricing Financial Derivatives
• Pricing Derivative Securities Using Integrated Quasi--Monte Carlo Methods with Dimension Reduction and Discontinuity Realignment
• Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction
• Multilevel Monte Carlo Path Simulation
• A Global Adaptive Quasi-Monte Carlo Algorithm for Functions of Low Truncation Dimension Applied to Problems from Finance
• Valuing American Options by Simulation: A Simple Least-Squares Approach

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