Economics at your fingertips  


Giorgio Pauletto ()

No 286, Computing in Economics and Finance 2000 from Society for Computational Economics

Abstract: Monte Carlo (MC) methods have proved flexible, robust and very useful techniques in computational finance. Several studies have investigated ways to achieve greater efficiency for such methods for serial computers.In this paper, we concentrate on the parallelization potentials of the MC methods. While MC is generally thought to be `embarrassingly parallel', the results eventually depend on the quality of the underlying parallel pseudo-random number generators. There are several methods for obtaining pseudo-random numbers on a parallel computer and we briefly present some alternatives. Then, we turn to an application of security pricing where we empirically investigate the pros and cons of the different generators. This also allows us to assess the advantages or inconveniences of parallel MC versus its serial version in the computational finance framework.

Date: 2000-07-05
References: Add references at CitEc
Citations: Track citations by RSS feed

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Computing in Economics and Finance 2000 from Society for Computational Economics CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain. Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().

Page updated 2021-01-14
Handle: RePEc:sce:scecf0:286