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Pricing barrier options in the Heston model using the Heath–Platen estimator

Author

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  • Coskun Sema

    (Department of Mathematics, University of Kaiserslautern, 67663 Kaiserlautern, Germany)

  • Korn Ralf

    (Department of Mathematics/Financial Mathematics Group, University of Kaiserslautern/Fraunhofer ITWM, 67663 Kaiserlautern, Germany)

Abstract

Both barrier options and the Heston stochastic volatility model are omnipresent in real-life applications of financial mathematics. In this paper, we apply the Heath–Platen (HP) estimator (as first introduced by Heath and Platen in [12]) to price barrier options in the Heston model setting as an alternative to conventional Monte Carlo methods and PDE based methods. We demonstrate the superior performance of the HP estimator via numerical examples and explain this performance by a detailed look at the underlying theoretical concept of the HP estimator.

Suggested Citation

  • Coskun Sema & Korn Ralf, 2018. "Pricing barrier options in the Heston model using the Heath–Platen estimator," Monte Carlo Methods and Applications, De Gruyter, vol. 24(1), pages 29-41, March.
    • Handle: RePEc:bpj:mcmeap:v:24:y:2018:i:1:p:29-41:n:4
    DOI: 10.1515/mcma-2018-0004
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    References listed on IDEAS

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    1. Roger Lord & Remmert Koekkoek & Dick Van Dijk, 2010. "A comparison of biased simulation schemes for stochastic volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 177-194.
    2. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    3. Jean-Pierre Fouque & Tracey Andrew Tullie, 2002. "Variance reduction for Monte Carlo simulation in a stochastic volatility environment," Quantitative Finance, Taylor & Francis Journals, vol. 2(1), pages 24-30.
    4. David Heath & Eckhard Platen, 2014. "A Monte Carlo Method using PDE Expansions for a Diversifed Equity Index Model," Research Paper Series 350, Quantitative Finance Research Centre, University of Technology, Sydney.
    5. Mark Broadie & Özgür Kaya, 2006. "Exact Simulation of Stochastic Volatility and Other Affine Jump Diffusion Processes," Operations Research, INFORMS, vol. 54(2), pages 217-231, April.
    6. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    7. David Heath & Eckhard Platen, 2002. "A variance reduction technique based on integral representations," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 362-369.
    8. Jean-Pierre Fouque & Chuan-Hsiang Han, 2004. "Variance reduction for Monte Carlo methods to evaluate option prices under multi-factor stochastic volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 4(5), pages 597-606.
    9. Mark Broadie & Paul Glasserman & Steven Kou, 1997. "A Continuity Correction for Discrete Barrier Options," Mathematical Finance, Wiley Blackwell, vol. 7(4), pages 325-349, October.
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