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Option Pricing For Garch Models With Markov Switching

Author

Listed:
  • ROBERT J. ELLIOTT

    (Haskayne School of Business, University of Calgary, T2N 1N4 Calgary, Canada)

  • TAK KUEN SIU

    (Department of Actuarial Mathematics and Statistics, Heriot-Watt University, EH14 4AS Edinburgh, UK)

  • LEUNGLUNG CHAN

    (Department of Mathematics and Statistics, University of Calgary, T2N 1N4 Calgary, Canada)

Abstract

In this paper we develop a method for pricing derivatives under a Markov switching version of the Heston-Nandi GARCH (1, 1) model by using a well known tool from actuarial science, namely the Esscher transform. We suppose that the dynamics of the GARCH process switch over time according to one of the regimes described by the states of an observable Markov chain process. By augmenting the conditional Esscher transform with the observable Markov switching process, a Markov switching conditional Esscher transform (MSCET) is developed to identify a martingale measure for option valuation in the incomplete market described by our model. We provide an alternative approach for the derivation of an analytical option valuation formula under the Markov switching Heston-Nandi GARCH (1, 1) model. The use of the MSCET can be justified by considering a utility maximization problem with respect to a power utility function associated with the Markov switching risk-averse parameters.

Suggested Citation

  • Robert J. Elliott & Tak Kuen Siu & Leunglung Chan, 2006. "Option Pricing For Garch Models With Markov Switching," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 9(06), pages 825-841.
    • Handle: RePEc:wsi:ijtafx:v:09:y:2006:i:06:n:s0219024906003846
    DOI: 10.1142/S0219024906003846
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    Citations

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    Cited by:

    1. Rombouts, Jeroen V.K. & Stentoft, Lars, 2014. "Bayesian option pricing using mixed normal heteroskedasticity models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 588-605.
    2. H. Bertholon & A. Monfort & F. Pegoraro, 2008. "Econometric Asset Pricing Modelling," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 407-458, Fall.
    3. Escobar-Anel, Marcos & Rastegari, Javad & Stentoft, Lars, 2021. "Option pricing with conditional GARCH models," European Journal of Operational Research, Elsevier, vol. 289(1), pages 350-363.
    4. Lau, John W. & Siu, Tak Kuen, 2008. "On option pricing under a completely random measure via a generalized Esscher transform," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 99-107, August.
    5. Chen Tong & Peter Reinhard Hansen & Zhuo Huang, 2022. "Option pricing with state‐dependent pricing kernel," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1409-1433, August.
    6. Michael C. Fu & Bingqing Li & Rongwen Wu & Tianqi Zhang, 2020. "Option Pricing Under a Discrete-Time Markov Switching Stochastic Volatility with Co-Jump Model," Papers 2006.15054, arXiv.org.
    7. Badescu, Alex & Elliott, Robert J. & Siu, Tak Kuen, 2009. "Esscher transforms and consumption-based models," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 337-347, December.
    8. Kiyotaka Satoyoshi & Hidetoshi Mitsui, 2011. "Empirical Study of Nikkei 225 Options with the Markov Switching GARCH Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 18(1), pages 55-68, March.

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