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Variance-constrained canonical least-squares Monte Carlo: An accurate method for pricing American options

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  • Liu, Qiang
  • Guo, Shuxin

Abstract

The pricing accuracy of the canonical least-squares Monte Carlo (CLM) method can be improved significantly by incorporating innovatively a variance constraint in the derivation of the canonical risk-neutral distribution. This new approach is called the variance-constrained CLM (vCLM) in the paper. Operationally, the forward variance is set to be the square of the volatility implied under vCLM by the option's market price from a previous trading day. For 16,249 American-style S&P 100 index puts, vCLM produced an average absolute pricing error of 5.94%, easily outperforming CLM, a competing nonparametric approach, and a GARCH-based benchmark.

Suggested Citation

  • Liu, Qiang & Guo, Shuxin, 2014. "Variance-constrained canonical least-squares Monte Carlo: An accurate method for pricing American options," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 77-89.
    • Handle: RePEc:eee:ecofin:v:28:y:2014:i:c:p:77-89
    DOI: 10.1016/j.najef.2014.02.002
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    References listed on IDEAS

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    Cited by:

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    2. Liu, Qiang & Guo, Shuxin & Qiao, Gaoxiu, 2015. "VIX forecasting and variance risk premium: A new GARCH approach," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 314-322.

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    More about this item

    Keywords

    Canonical least-squares Monte Carlo; Variance constraint; Implied volatility; American-style S&P 100 index put; Numerical measure change;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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