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

Research output: Contribution to journalArticle

21 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)641-653
Number of pages13
JournalParallel Computing
Volume26
Issue number5
DOIs
StatePublished - Jan 1 2000

Fingerprint

(t, m, s)-nets
Quasi-Monte Carlo
Monte Carlo Algorithm
Parallel processing systems
Parallel Computing
Parallel algorithms
Resource allocation
Scalability
Monte Carlo methods
Derivatives
Adaptive Routing
Dynamic Routing
Derivative
Load Balancing
Parallel Algorithms
Monte Carlo method
High-dimensional
Approximation

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

Cite this

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Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives. / Li, Jenny Xiaoe; Mullen, Gary Lee.

In: Parallel Computing, Vol. 26, No. 5, 01.01.2000, p. 641-653.

Research output: Contribution to journalArticle

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