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Optimal search for parameters in Monte Carlo simulation for derivative pricing

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  • Wang, Chuan-Ju
  • Kao, Ming-Yang

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.

Suggested Citation

  • Wang, Chuan-Ju & Kao, Ming-Yang, 2016. "Optimal search for parameters in Monte Carlo simulation for derivative pricing," European Journal of Operational Research, Elsevier, vol. 249(2), pages 683-690.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:683-690
    DOI: 10.1016/j.ejor.2015.08.060
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    References listed on IDEAS

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    2. Wei-Cheng Chen & Wei-Ho Chung, 2019. "Option Pricing via Multi-path Autoregressive Monte Carlo Approach," Papers 1906.06483, arXiv.org.

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