IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v16y2016i2p247-257.html
   My bibliography  Save this article

Efficient calculation of the Greeks for exponential Lévy processes: an application of measure valued differentiation

Author

Listed:
  • Georg Ch. Pflug
  • Philipp Thoma

Abstract

Monte Carlo simulation methods have become more and more important in the financial sector in the past years. In this paper, we introduce a new simulation method for the estimation of the derivatives of prices of financial contracts with respect to (w.r.t.) certain distributional parameters called the ‘Greeks’. In particular, we assume that the underlying financial process is a Lévy-type process in discrete time. Our method is based on the Measure-Valued Differentiation (MVD) approach, which allows representation of derivatives as differences of two processes, called the phantoms. We discuss the applicability of MVD for different types of option pay-offs in combination with different types of models of the underlying and provide a framework for the applicability of MVD for path-dependent pay-off functions, as Lookback Options or Asian Options.

Suggested Citation

  • Georg Ch. Pflug & Philipp Thoma, 2016. "Efficient calculation of the Greeks for exponential Lévy processes: an application of measure valued differentiation," Quantitative Finance, Taylor & Francis Journals, vol. 16(2), pages 247-257, February.
  • Handle: RePEc:taf:quantf:v:16:y:2016:i:2:p:247-257
    DOI: 10.1080/14697688.2015.1114364
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2015.1114364
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/14697688.2015.1114364?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Montero, Miquel & Kohatsu-Higa, Arturo, 2003. "Malliavin Calculus applied to finance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 548-570.
    2. Heidergott, Bernd & Vazquez-Abad, Felisa J. & Volk-Makarewicz, Warren, 2008. "Sensitivity estimation for Gaussian systems," European Journal of Operational Research, Elsevier, vol. 187(1), pages 193-207, May.
    3. Bernd Heidergott & Haralambie Leahu, 2010. "Weak Differentiability of Product Measures," Mathematics of Operations Research, INFORMS, vol. 35(1), pages 27-51, February.
    4. Peter W. Glynn & Ward Whitt, 1992. "The Asymptotic Efficiency of Simulation Estimators," Operations Research, INFORMS, vol. 40(3), pages 505-520, June.
    5. Arturo Kohatsu & Montero Miquel, 2003. "Malliavin calculus in finance," Economics Working Papers 672, Department of Economics and Business, Universitat Pompeu Fabra.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anselm Hudde & Ludger Rüschendorf, 2023. "European and Asian Greeks for Exponential Lévy Processes," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-24, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nicola Cufaro Petroni & Piergiacomo Sabino, 2013. "Multidimensional quasi-Monte Carlo Malliavin Greeks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 36(2), pages 199-224, November.
    3. Zhenyu Cui & Michael C. Fu & Jian-Qiang Hu & Yanchu Liu & Yijie Peng & Lingjiong Zhu, 2020. "On the Variance of Single-Run Unbiased Stochastic Derivative Estimators," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 390-407, April.
    4. Boyle, Phelim & Potapchik, Alexander, 2008. "Prices and sensitivities of Asian options: A survey," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 189-211, February.
    5. Giesecke, K. & Schwenkler, G., 2019. "Simulated likelihood estimators for discretely observed jump–diffusions," Journal of Econometrics, Elsevier, vol. 213(2), pages 297-320.
    6. Chao Yu & Yuhan Cheng, 2023. "Malliavin Calculus and Its Application to Robust Optimal Investment for an Insider," Mathematics, MDPI, vol. 11(20), pages 1-38, October.
    7. Anselm Hudde & Ludger Rüschendorf, 2023. "European and Asian Greeks for Exponential Lévy Processes," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-24, March.
    8. Weber, Florian & Schmid, Thomas & Pietz, Matthäus & Kaserer, Christoph, 2010. "Simulation-based valuation of project finance: does model complexity really matter?," CEFS Working Paper Series 2010-03, Technische Universität München (TUM), Center for Entrepreneurial and Financial Studies (CEFS).
    9. Nanjing Jian & Shane G. Henderson, 2020. "Estimating the Probability that a Function Observed with Noise Is Convex," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 376-389, April.
    10. Andersson, Patrik & Kohatsu-Higa, Arturo & Yuasa, Tomooki, 2020. "Second order probabilistic parametrix method for unbiased simulation of stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 130(9), pages 5543-5574.
    11. Muroi, Yoshifumi & Suda, Shintaro, 2017. "Computation of Greeks in jump-diffusion models using discrete Malliavin calculus," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 140(C), pages 69-93.
    12. Nicola Cufaro Petroni & Piergiacomo Sabino, 2013. "Pricing and Hedging Asian Basket Options with Quasi-Monte Carlo Simulations," Methodology and Computing in Applied Probability, Springer, vol. 15(1), pages 147-163, March.
    13. Cliff C Kerr & Salvador Dura-Bernal & Tomasz G Smolinski & George L Chadderdon & David P Wilson, 2018. "Optimization by Adaptive Stochastic Descent," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-16, March.
    14. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin & Tim Zajic, 1999. "Multilevel Splitting for Estimating Rare Event Probabilities," Operations Research, INFORMS, vol. 47(4), pages 585-600, August.
    15. Cui, Zhenyu & Fu, Michael C. & Peng, Yijie & Zhu, Lingjiong, 2020. "Optimal unbiased estimation for expected cumulative discounted cost," European Journal of Operational Research, Elsevier, vol. 286(2), pages 604-618.
    16. Koch, Erwan & Robert, Christian Y., 2022. "Stochastic derivative estimation for max-stable random fields," European Journal of Operational Research, Elsevier, vol. 302(2), pages 575-588.
    17. Leão, Dorival & Ohashi, Alberto, 2010. "Weak Approximations for Wiener Functionals," Insper Working Papers wpe_215, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    18. Jose Blanchet & Juan Li & Marvin K. Nakayama, 2019. "Rare-Event Simulation for Distribution Networks," Operations Research, INFORMS, vol. 67(5), pages 1383-1396, September.
    19. Bernd Heidergott & Warren Volk-Makarewicz, 2013. "A Measure-Valued Differentiation Approach to Sensitivity Analysis of Quantiles," Tinbergen Institute Discussion Papers 13-082/III, Tinbergen Institute.
    20. Leão, Dorival & Ohashi, Alberto, 2012. "Weak Approximations for Wiener Functionals," Insper Working Papers wpe_276, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:16:y:2016:i:2:p:247-257. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.