Skip to Main content Skip to Navigation
New interface

Parallel computing for linear, nonlinear and linear inverse problems in finance

Abstract : Handling multidimensional parabolic linear, nonlinear and linear inverse problems is the main objective of this work. It is the multidimensional word that makes virtually inevitable the use of simulation methods based on Monte Carlo. This word also makes necessary the use of parallel architectures. Indeed, the problems dealing with a large number of assets are major resources consumers, and only parallelization is able to reduce their execution times. Consequently, the first goal of our work is to propose "appropriate" random number generators to parallel and massively parallel architecture implemented on CPUs/GPUs cluster. We quantify the speedup and the energy consumption of the parallel execution of a European pricing. The second objective is to reformulate the nonlinear problem of pricing American options in order to get the same parallelization gains as those obtained for linear problems. In addition to its parallelization suitability, the proposed method based on Malliavin calculus has other practical advantages. Continuing with parallel algorithms, the last point of this work is dedicated to the uniqueness of the solution of some linear inverse problems in finance. This theoretical study enables the use of simple methods based on Monte Carlo
Complete list of metadata

Cited literature [54 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Thursday, January 21, 2016 - 2:40:06 PM
Last modification on : Saturday, January 15, 2022 - 4:13:39 AM
Long-term archiving on: : Friday, November 11, 2016 - 2:33:34 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01260067, version 1



Lokman Abbas-Turki. Parallel computing for linear, nonlinear and linear inverse problems in finance. Computational Finance [q-fin.CP]. Université Paris-Est, 2012. English. ⟨NNT : 2012PEST1055⟩. ⟨tel-01260067⟩



Record views


Files downloads