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Deep Importance Sampling

Author

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  • Benjamin Virrion

    (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

We present a generic path-dependent importance sampling algorithm where the Girsanov induced change of probability on the path space is represented by a sequence of neural networks taking the past of the trajectory as an input. At each learning step, the neural networks' parameters are trained so as to reduce the variance of the Monte Carlo estimator induced by this change of measure. This allows for a generic path dependent change of measure which can be used to reduce the variance of any path-dependent financial payoff. We show in our numerical experiments that for payoffs consisting of either a call, an asymmetric combination of calls and puts, a symmetric combination of calls and puts, a multi coupon autocall or a single coupon autocall, we are able to reduce the variance of the Monte Carlo estimators by factors between 2 and 9. The numerical experiments also show that the method is very robust to changes in the parameter values, which means that in practice, the training can be done offline and only updated on a weekly basis.

Suggested Citation

  • Benjamin Virrion, 2020. "Deep Importance Sampling," Working Papers hal-02887331, HAL.
    • Handle: RePEc:hal:wpaper:hal-02887331
    Note: View the original document on HAL open archive server: https://hal.science/hal-02887331v2
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    References listed on IDEAS

    as
    1. Jim Gatheral & Antoine Jacquier, 2014. "Arbitrage-free SVI volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 59-71, January.
    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. Benjamin Virrion, 2020. "Deep Importance Sampling," Papers 2007.02692, arXiv.org, revised Jul 2020.
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    Cited by:

    1. Benjamin Virrion, 2020. "Deep Importance Sampling," Papers 2007.02692, arXiv.org, revised Jul 2020.

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    More about this item

    Keywords

    Importance Sampling; Path-Dependence; Neural Networks;
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