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Kernel Estimation of the Greeks for Options with Discontinuous Payoffs

Author

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  • Guangwu Liu

    (Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong, China)

  • L. Jeff Hong

    (Department of Industrial Engineering and Logistics Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China)

Abstract

The Greeks are the derivatives (also known as sensitivities) of the option prices with respect to market parameters. They play an important role in financial risk management. Among many Monte Carlo methods of estimating the Greeks, the classical pathwise method requires only the pathwise information that is directly observable from simulation and is generally easier to implement than many other methods. However, the classical pathwise method is generally not applicable to the Greeks of options with discontinuous payoffs and the second-order Greeks. In this paper, we generalize the classical pathwise method to allow discontinuity in the payoffs. We show how to apply the new pathwise method to the first- and second-order Greeks and propose kernel estimators that require little analytical efforts and are very easy to implement. The numerical results show that our estimators work well for practical problems.

Suggested Citation

  • Guangwu Liu & L. Jeff Hong, 2011. "Kernel Estimation of the Greeks for Options with Discontinuous Payoffs," Operations Research, INFORMS, vol. 59(1), pages 96-108, February.
  • Handle: RePEc:inm:oropre:v:59:y:2011:i:1:p:96-108
    DOI: 10.1287/opre.1100.0844
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    References listed on IDEAS

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    1. Zhiyong Chen & Paul Glasserman, 2008. "Sensitivity estimates for portfolio credit derivatives using Monte Carlo," Finance and Stochastics, Springer, vol. 12(4), pages 507-540, October.
    2. Guangwu Liu & Liu Jeff Hong, 2009. "Kernel estimation of quantile sensitivities," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(6), pages 511-525, September.
    3. Mark Broadie & Paul Glasserman, 1996. "Estimating Security Price Derivatives Using Simulation," Management Science, INFORMS, vol. 42(2), pages 269-285, February.
    4. L. Jeff Hong, 2009. "Estimating Quantile Sensitivities," Operations Research, INFORMS, vol. 57(1), pages 118-130, February.
    5. Chen, Nan & Glasserman, Paul, 2007. "Malliavin Greeks without Malliavin calculus," Stochastic Processes and their Applications, Elsevier, vol. 117(11), pages 1689-1723, November.
    6. Guillaume Bernis & Emmanuel Gobet & Arturo Kohatsu‐Higa, 2003. "Monte Carlo Evaluation of Greeks for Multidimensional Barrier and Lookback Options," Mathematical Finance, Wiley Blackwell, vol. 13(1), pages 99-113, January.
    7. Michael C. Fu & L. Jeff Hong & Jian-Qiang Hu, 2009. "Conditional Monte Carlo Estimation of Quantile Sensitivities," Management Science, INFORMS, vol. 55(12), pages 2019-2027, December.
    8. L. Jeff Hong & Guangwu Liu, 2010. "Pathwise Estimation of Probability Sensitivities Through Terminating or Steady-State Simulations," Operations Research, INFORMS, vol. 58(2), pages 357-370, April.
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    Citations

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    Cited by:

    1. Christian Walter, 2020. "Sustainable Financial Risk Modelling Fitting the SDGs: Some Reflections," Sustainability, MDPI, vol. 12(18), pages 1-28, September.
    2. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    3. L. Jeff Hong & Sandeep Juneja & Jun Luo, 2014. "Estimating Sensitivities of Portfolio Credit Risk Using Monte Carlo," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 848-865, November.
    4. Guangxin Jiang & L. Jeff Hong & Barry L. Nelson, 2020. "Online Risk Monitoring Using Offline Simulation," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 356-375, April.
    5. Joshi, Mark S. & Zhu, Dan, 2016. "An exact method for the sensitivity analysis of systems simulated by rejection techniques," European Journal of Operational Research, Elsevier, vol. 254(3), pages 875-888.
    6. Bernd Heidergott & Warren Volk-Makarewicz, 2016. "A Measure-Valued Differentiation Approach to Sensitivities of Quantiles," Mathematics of Operations Research, INFORMS, vol. 41(1), pages 293-317, February.
    7. Xin Yun & L. Jeff Hong & Guangxin Jiang & Shouyang Wang, 2019. "On gamma estimation via matrix kriging," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(5), pages 393-410, August.
    8. Nan Chen & Yanchu Liu, 2014. "American Option Sensitivities Estimation via a Generalized Infinitesimal Perturbation Analysis Approach," Operations Research, INFORMS, vol. 62(3), pages 616-632, June.
    9. Wei Yuan & Ahmet Göncü & Giray Ökten, 2015. "Estimating sensitivities of temperature-based weather derivatives," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1942-1955, April.
    10. Roberto Daluiso & Giorgio Facchinetti, 2018. "Algorithmic Differentiation For Discontinuous Payoffs," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-41, June.
    11. Yongqiang Wang & Michael C. Fu & Steven I. Marcus, 2012. "A New Stochastic Derivative Estimator for Discontinuous Payoff Functions with Application to Financial Derivatives," Operations Research, INFORMS, vol. 60(2), pages 447-460, April.
    12. Yijie Peng & Li Xiao & Bernd Heidergott & L. Jeff Hong & Henry Lam, 2022. "A New Likelihood Ratio Method for Training Artificial Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 638-655, January.
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    14. Shaolong Tong & Guangwu Liu, 2016. "Importance Sampling for Option Greeks with Discontinuous Payoffs," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 223-235, May.

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    Keywords

    finance; securities;

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