ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Original Paper

A simulation approach to financial options Greeks estimation under Lévy processes

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.3.009
  • Received Date: 22 January 2016
  • Accepted Date: 10 May 2016
  • Rev Recd Date: 10 May 2016
  • Publish Date: 30 March 2017
  • Accurate estimation of the Greeks for financial options is an important practical procedure for risk management of financial derivatives. It is also an important topic in financial engineering research. Monte Carlo simulation method, being capable of avoiding the problem of “curse of dimensionality”, is one of the most popular computational tools in financial engineering. Here a new Monte Carlo simulation method was developed to estimate Greeks for financial options under Lévy processes. For asset price models following Lévy processes, only the characteristic functions are known. By building our method on Fourier transform inversion and linear interpolations, approximations of the cumulative distribution functions and the probability density functions can be obtained, paving the way for generating random samples and constructing Monte Carlo simulation estimates to the Greeks. Numerical experiments were conducted to illustrate the efficiency of the proposed method and the results show that it performs more efficiently than alternatives in the literature.
    Accurate estimation of the Greeks for financial options is an important practical procedure for risk management of financial derivatives. It is also an important topic in financial engineering research. Monte Carlo simulation method, being capable of avoiding the problem of “curse of dimensionality”, is one of the most popular computational tools in financial engineering. Here a new Monte Carlo simulation method was developed to estimate Greeks for financial options under Lévy processes. For asset price models following Lévy processes, only the characteristic functions are known. By building our method on Fourier transform inversion and linear interpolations, approximations of the cumulative distribution functions and the probability density functions can be obtained, paving the way for generating random samples and constructing Monte Carlo simulation estimates to the Greeks. Numerical experiments were conducted to illustrate the efficiency of the proposed method and the results show that it performs more efficiently than alternatives in the literature.
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  • [1]
    GLASSERMAN P. Monte Carlo Method in Financial Engineering [M]. New York : Springer, 2004: 386-396.
    [2]
    GLASSERMAN P, LIU Z J. Sensitivity estimation from characteristic functions[J]. Operations Research, 2010, 58(6): 1 611-1 623.
    [3]
    LIU G W, HONG L. Kernel estimation of the Greeks for options with discontinuous payoffs [J]. Operations Research, 2011, 59(1): 96-108.
    [4]
    CHEN N, LIU Y C. American option sensitivities estimation via a generalized perturbation analysis approach [J]. Operations Research, 2014, 62(3): 616-632.
    [5]
    FENG L M, LIN X. Inverting analytic characteristic functions and financial applications[J]. SIAM Journal on Financial Mathematics, 2013, 4(1): 372-398.
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Catalog

    [1]
    GLASSERMAN P. Monte Carlo Method in Financial Engineering [M]. New York : Springer, 2004: 386-396.
    [2]
    GLASSERMAN P, LIU Z J. Sensitivity estimation from characteristic functions[J]. Operations Research, 2010, 58(6): 1 611-1 623.
    [3]
    LIU G W, HONG L. Kernel estimation of the Greeks for options with discontinuous payoffs [J]. Operations Research, 2011, 59(1): 96-108.
    [4]
    CHEN N, LIU Y C. American option sensitivities estimation via a generalized perturbation analysis approach [J]. Operations Research, 2014, 62(3): 616-632.
    [5]
    FENG L M, LIN X. Inverting analytic characteristic functions and financial applications[J]. SIAM Journal on Financial Mathematics, 2013, 4(1): 372-398.

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