GPU acceleration of the stochastic grid bundling method for early-exercise options

DOI10.1080/00207160.2015.1067689zbMath1335.91105OpenAlexW1857288043MaRDI QIDQ2804499

Álvaro Leitao, Cornelis W. Oosterlee

Publication date: 29 April 2016

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: https://ir.cwi.nl/pub/23949

zbMATH Keywords

Monte Carlo simulationparallel programminghigh performance computingGPGPUcomputational financeleast-squares regressioncompute unified device architecture (CUDA)basket Bermudan optionsearly-exercise derivativeshigh-dimensional pricingstochastic grid bundling method (SGBM)

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Monte Carlo methods (65C05) Derivative securities (option pricing, hedging, etc.) (91G20) Parallel numerical computation (65Y05) Random number generation in numerical analysis (65C10)

Related Items (6)

Stochastic grid bundling method for backward stochastic differential equations ⋮ A general continuous time Markov chain approximation for multi-asset option pricing with systems of correlated diffusions ⋮ Calculation of Exposure Profiles and Sensitivities of Options under the Heston and the Heston Hull-White Models ⋮ Modern Monte Carlo Methods and GPU Computing ⋮ An SGBM-XVA demonstrator: a scalable Python tool for pricing XVA ⋮ Explainable neural network for pricing and universal static hedging of contingent claims

Uses Software

• CUDA

Cites Work

• Unnamed Item
• Pricing early-exercise and discrete barrier options by Fourier-cosine series expansions
• Probability, Random Processes, and Statistical Analysis
• Pricing American Options: A Duality Approach
• Monte Carlo valuation of American options
• Selected Topics in Characteristic Functions
• Numerical Computations with GPUs
• Valuing American Options by Simulation: A Simple Least-Squares Approach

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