Optimal search for parameters in Monte Carlo simulation for derivative pricing

Chuan Ju Wang*, Ming-Yang Kao

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper provides a novel and general framework for the problem of searching parameter space in Monte Carlo simulations. We propose a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values for derivative pricing which are needed to achieve desired precisions. We also give the competitive ratios of the two algorithms and prove the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.

Original languageEnglish (US)
Pages (from-to)683-690
Number of pages8
JournalEuropean Journal of Operational Research
Volume249
Issue number2
DOIs
StatePublished - Mar 1 2016

Fingerprint

Pricing
Monte Carlo Simulation
Online Algorithms
Derivatives
Derivative
Costs
Competitive Ratio
Deterministic Algorithm
Randomized Algorithms
Parameter Space
Optimality
Experimental Results
Monte Carlo simulation
Derivative pricing
Online algorithms
Framework
Competitive ratio

Keywords

  • Competitive ratio
  • Deterministic online algorithm
  • Finance
  • Monte Carlo simulation
  • Randomized online algorithm

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

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title = "Optimal search for parameters in Monte Carlo simulation for derivative pricing",
abstract = "This paper provides a novel and general framework for the problem of searching parameter space in Monte Carlo simulations. We propose a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values for derivative pricing which are needed to achieve desired precisions. We also give the competitive ratios of the two algorithms and prove the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.",
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Optimal search for parameters in Monte Carlo simulation for derivative pricing. / Wang, Chuan Ju; Kao, Ming-Yang.

In: European Journal of Operational Research, Vol. 249, No. 2, 01.03.2016, p. 683-690.

Research output: Contribution to journalArticle

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AU - Wang, Chuan Ju

AU - Kao, Ming-Yang

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N2 - This paper provides a novel and general framework for the problem of searching parameter space in Monte Carlo simulations. We propose a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values for derivative pricing which are needed to achieve desired precisions. We also give the competitive ratios of the two algorithms and prove the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.

AB - This paper provides a novel and general framework for the problem of searching parameter space in Monte Carlo simulations. We propose a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values for derivative pricing which are needed to achieve desired precisions. We also give the competitive ratios of the two algorithms and prove the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.

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KW - Deterministic online algorithm

KW - Finance

KW - Monte Carlo simulation

KW - Randomized online algorithm

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