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Pricing American-style Derivatives under the Heston Model Dynamics: A Fast Fourier Transformation in the Geske–Johnson Scheme

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  • Oleksandr Zhylyevskyy

Abstract

Theoretical research on option valuation tends to focus on pricing the plain-vanilla European-style derivatives. Duffie, Pan, and Singleton (Econometrica, 2000) have recently developed a general transform method to determine the value of European options for a broad class of the underlying price dynamics. Contrastingly, no universal and analytically attractive approach to pricing of American-style derivatives is yet available. When the underlying price follows simple dynamics, literature suggests using finite difference methods. Simulation methods are often applied in more complicated cases. This paper addresses the valuation of American-style derivatives when the price of an underlying asset follows the Heston model dynamics (Rev.Fin.S., 1993). The model belongs to the class of stochastic volatility models, which have been proposed in the hope of remedying the strike-price biases of the Black–Scholes formula. Option values are obtained by a variant of the Geske–Johnson scheme (JF, 1984), which has been devised in the context of the Black–Scholes model. The scheme exploits the fact that an American option is the limit of a sequence of “Bermudan†derivatives. The latter ones can be priced recursively according to a simple formula, and iterations start from valuing a corresponding European-style security. To implement the recursion, one needs to obtain the expected value of “Bermudan†prices in the joint measure of the state variables of the model. Since the joint density must be, in turn, recovered by inverting the joint characteristic function, an unmodified Geske–Johnson algorithm implies a computationally unfeasible multiple integration. To drastically reduce the cost of numerical integration, I suggest applying a kernel-smoothed bivariate fast Fourier transformation to obtain the density function. Numerical accuracy of the method is assessed by predicting option prices of the S&P 100 index options

Suggested Citation

  • Oleksandr Zhylyevskyy, 2005. "Pricing American-style Derivatives under the Heston Model Dynamics: A Fast Fourier Transformation in the Geske–Johnson Scheme," Computing in Economics and Finance 2005 187, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:187
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    References listed on IDEAS

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    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    3. 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.
    4. Bates, David S, 1996. "Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options," The Review of Financial Studies, Society for Financial Studies, vol. 9(1), pages 69-107.
    5. Geske, Robert & Johnson, Herb E, 1984. "The American Put Option Valued Analytically," Journal of Finance, American Finance Association, vol. 39(5), pages 1511-1524, December.
    6. Chernov, Mikhail & Ghysels, Eric, 2000. "A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation," Journal of Financial Economics, Elsevier, vol. 56(3), pages 407-458, June.
    7. Broadie, Mark & Detemple, Jerome, 1996. "American Option Valuation: New Bounds, Approximations, and a Comparison of Existing Methods," The Review of Financial Studies, Society for Financial Studies, vol. 9(4), pages 1211-1250.
    8. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
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    Cited by:

    1. Song-Ping Zhu, 2011. "On Various Quantitative Approaches For Pricing American Options," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 313-332.

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    More about this item

    Keywords

    American-style option; stochastic volatility model; Geske–Johnson scheme; characteristic function inversion; fast Fourier transform;
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    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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