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Delta force: option pricing with differential machine learning

Author

Listed:
  • Magnus Grønnegaard Frandsen

    (University of Copenhagen)

  • Tobias Cramer Pedersen

    (University of Copenhagen)

  • Rolf Poulsen

    (University of Copenhagen)

Abstract

We show how and why to use a financially meaningful differential regularization method when pricing options by Monte Carlo simulation, be that in polynomial regression or neural network context.

Suggested Citation

  • Magnus Grønnegaard Frandsen & Tobias Cramer Pedersen & Rolf Poulsen, 2022. "Delta force: option pricing with differential machine learning," Digital Finance, Springer, vol. 4(1), pages 1-15, March.
    • Handle: RePEc:spr:digfin:v:4:y:2022:i:1:d:10.1007_s42521-021-00041-7
    DOI: 10.1007/s42521-021-00041-7
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    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Boyle, Phelim & Broadie, Mark & Glasserman, Paul, 1997. "Monte Carlo methods for security pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1267-1321, June.
    3. Mark Broadie & Paul Glasserman, 1996. "Estimating Security Price Derivatives Using Simulation," Management Science, INFORMS, vol. 42(2), pages 269-285, February.
    4. Brian Huge & Antoine Savine, 2020. "Differential Machine Learning," Papers 2005.02347, arXiv.org, revised Sep 2020.
    5. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Pedro Duarte Gomes, 2024. "Mathematics of Differential Machine Learning in Derivative Pricing and Hedging," Papers 2405.01233, arXiv.org.

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    More about this item

    Keywords

    Differential machine learning; option pricing;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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