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Smoothly truncated stable distributions, GARCH-models, and option pricing

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  • Christian Menn
  • Svetlozar Rachev

Abstract

Although asset return distributions are known to be conditionally leptokurtic, this fact has rarely been addressed in the recent GARCH model literature. For this reason, we introduce the class of smoothly truncated stable distributions (STS distributions) and derive a generalized GARCH option pricing framework based on non-Gaussian innovations. Our empirical results show that (1) the model’s performance in the objective as well as the risk-neutral world is substantially improved by allowing for non-Gaussian innovations and (2) the model’s best option pricing performance is achieved with a new estimation approach where all model parameters are obtained from time-series information whereas the market price of risk and the spot variance are inverted from market prices of options. Copyright Springer-Verlag 2009

Suggested Citation

  • Christian Menn & Svetlozar Rachev, 2009. "Smoothly truncated stable distributions, GARCH-models, and option pricing," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 411-438, July.
    • Handle: RePEc:spr:mathme:v:69:y:2009:i:3:p:411-438
    DOI: 10.1007/s00186-008-0245-6
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    Cited by:

    1. Young Kim & Frank Fabozzi & Zuodong Lin & Svetlozar Rachev, 2012. "Option pricing and hedging under a stochastic volatility Lévy process model," Review of Derivatives Research, Springer, vol. 15(1), pages 81-97, April.
    2. Kim, Young Shin & Rachev, Svetlozar T. & Bianchi, Michele Leonardo & Mitov, Ivan & Fabozzi, Frank J., 2011. "Time series analysis for financial market meltdowns," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1879-1891, August.
    3. Matthias R. Fengler & Alexander Melnikov, 2018. "GARCH option pricing models with Meixner innovations," Review of Derivatives Research, Springer, vol. 21(3), pages 277-305, October.
    4. Möller, Christoph & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "Balancing energy strategies in electricity portfolio management," Energy Economics, Elsevier, vol. 33(1), pages 2-11, January.
    5. Shin Kim, Young & Rachev, Svetlozar T. & Leonardo Bianchi, Michele & Fabozzi, Frank J., 2010. "Tempered stable and tempered infinitely divisible GARCH models," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2096-2109, September.
    6. Zhou, Xiaoping & Durfee, Antonina V. & Fabozzi, Frank J., 2016. "On stability of operational risk estimates by LDA: From causes to approaches," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 266-278.
    7. Mahdi Teimouri & Saralees Nadarajah, 2013. "On simulating truncated stable random variables," Computational Statistics, Springer, vol. 28(5), pages 2367-2377, October.
    8. Young Shin Kim & Hyangju Kim & Jaehyung Choi, 2023. "Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models," Papers 2303.08760, arXiv.org.
    9. Lorenzo Mercuri & Fabio Bellini, 2014. "Option Pricing in a Dynamic Variance-Gamma Model," Papers 1405.7342, arXiv.org.
    10. Fabio Bellini & Lorenzo Mercuri, 2014. "Option pricing in a conditional Bilateral Gamma model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 373-390, June.

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