Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives

Jenny X. Li; Gary L. Mullen

doi:10.1016/S0167-8191(99)00124-6

Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives

Jenny X. Li, Gary L. Mullen

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

The performance the standard Monte Carlo method is compared with the performance obtained through the use of (t,m,s)-nets in base b in the approximation of several high dimensional integral problems in valuing derivatives and other securities. The (t,m,s)-nets are generated by a parallel algorithm, where particular considerations are given to scalability of dynamic adaptive routing and load balancing in the design and implementation of the algorithm. From the numerical evidence it appears that such nets can be powerful tools for valuing such securities.

Original language	English (US)
Pages (from-to)	641-653
Number of pages	13
Journal	Parallel Computing
Volume	26
Issue number	5
DOIs	https://doi.org/10.1016/S0167-8191(99)00124-6
State	Published - Mar 2000

All Science Journal Classification (ASJC) codes

Software
Theoretical Computer Science
Hardware and Architecture
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Artificial Intelligence

Access to Document

10.1016/S0167-8191(99)00124-6

Cite this

@article{4cc0a807742a467a9e1b793205f3e814,

title = "Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives",

abstract = "The performance the standard Monte Carlo method is compared with the performance obtained through the use of (t,m,s)-nets in base b in the approximation of several high dimensional integral problems in valuing derivatives and other securities. The (t,m,s)-nets are generated by a parallel algorithm, where particular considerations are given to scalability of dynamic adaptive routing and load balancing in the design and implementation of the algorithm. From the numerical evidence it appears that such nets can be powerful tools for valuing such securities.",

author = "Li, {Jenny X.} and Mullen, {Gary L.}",

year = "2000",

month = mar,

doi = "10.1016/S0167-8191(99)00124-6",

language = "English (US)",

volume = "26",

pages = "641--653",

journal = "Parallel Computing",

issn = "0167-8191",

publisher = "Elsevier B.V.",

number = "5",

}

TY - JOUR

T1 - Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives

AU - Li, Jenny X.

AU - Mullen, Gary L.

PY - 2000/3

Y1 - 2000/3

N2 - The performance the standard Monte Carlo method is compared with the performance obtained through the use of (t,m,s)-nets in base b in the approximation of several high dimensional integral problems in valuing derivatives and other securities. The (t,m,s)-nets are generated by a parallel algorithm, where particular considerations are given to scalability of dynamic adaptive routing and load balancing in the design and implementation of the algorithm. From the numerical evidence it appears that such nets can be powerful tools for valuing such securities.

AB - The performance the standard Monte Carlo method is compared with the performance obtained through the use of (t,m,s)-nets in base b in the approximation of several high dimensional integral problems in valuing derivatives and other securities. The (t,m,s)-nets are generated by a parallel algorithm, where particular considerations are given to scalability of dynamic adaptive routing and load balancing in the design and implementation of the algorithm. From the numerical evidence it appears that such nets can be powerful tools for valuing such securities.

UR - http://www.scopus.com/inward/record.url?scp=0033888622&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033888622&partnerID=8YFLogxK

U2 - 10.1016/S0167-8191(99)00124-6

DO - 10.1016/S0167-8191(99)00124-6

M3 - Article

AN - SCOPUS:0033888622

SN - 0167-8191

VL - 26

SP - 641

EP - 653

JO - Parallel Computing

JF - Parallel Computing

IS - 5

ER -

Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this