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Pricing exotic options using MSL-MC


  • Klaus Schmitz Abe


Today, better numerical approximations are required for multi-dimensional SDEs to improve on the poor performance of the standard Monte Carlo pricing method. With this aim in mind, this paper presents a method (MSL-MC) to price exotic options using multi-dimensional SDEs (e.g. stochastic volatility models). Usually, it is the weak convergence property of numerical discretizations that is most important, because, in financial applications, one is mostly concerned with the accurate estimation of expected payoffs. However, in the recently developed Multilevel Monte Carlo path simulation method (ML-MC), the strong convergence property plays a crucial role. We present a modification to the ML-MC algorithm that can be used to achieve better savings. To illustrate these, various examples of exotic options are given using a wide variety of payoffs, stochastic volatility models and the new Multischeme Multilevel Monte Carlo method (MSL-MC). For standard payoffs, both European and Digital options are presented. Examples are also given for complex payoffs, such as combinations of European options (Butterfly Spread, Strip and Strap options). Finally, for path-dependent payoffs, both Asian and Variance Swap options are demonstrated. This research shows how the use of stochastic volatility models and the θ scheme can improve the convergence of the MSL-MC so that the computational cost to achieve an accuracy of O (ε) is reduced from O (ε-super-−3) to O (ε-super-−2) for a payoff under global and non-global Lipschitz conditions.

Suggested Citation

  • Klaus Schmitz Abe, 2011. "Pricing exotic options using MSL-MC," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1379-1392, October.
  • Handle: RePEc:taf:quantf:v:11:y:2011:i:9:p:1379-1392
    DOI: 10.1080/14697680903426565

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    References listed on IDEAS

    1. Alan L. Lewis, 2000. "Option Valuation under Stochastic Volatility," Option Valuation under Stochastic Volatility, Finance Press, number ovsv, June.
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