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Deep Importance Sampling. (2020). Virrion, Benjamin.
In: Papers.
RePEc:arx:papers:2007.02692.

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  1. Deep Importance Sampling. (2020). Virrion, Benjamin.
    In: Working Papers.
    RePEc:hal:wpaper:hal-02887331.

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References

References cited by this document

  1. Benjamin Virrion. Deep importance sampling code repository. https://github.com/bvirrion/ deep-importance-sampling, 2020. A Parameter Tables Parameter Value x0 1.0 σ 0.2 T 1. NT

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  3. Jim Gatheral and Antoine Jacquier. Arbitrage-free svi volatility surfaces. Quantitative Finance, 14(1):59–71, 2014.

  4. Jun S Liu. Monte Carlo strategies in scientific computing. Springer Science & Business Media, 2008.
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  5. Paul Glasserman. Monte Carlo methods in financial engineering, volume 53. Springer Science & Business Media, 2013.
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  6. Phelim Boyle, Mark Broadie, and Paul Glasserman. Monte carlo methods for security pricing. Journal of economic dynamics and control, 21(8-9):1267–1321, 1997.

  7. Rafael Balestro Dias da Silva. Backtesting svi parameterization of implied volatilities. Master’s thesis, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro, 2016.
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  8. Shixiang Shane Gu, Zoubin Ghahramani, and Richard E Turner. Neural adaptive sequential monte carlo. In Advances in neural information processing systems, pages 2629–2637, 2015.
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  9. Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, and Jan Novák. Neural importance sampling. arXiv preprint arXiv:1808.03856, 2018.
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  10. Vincent Lemaire, Gilles Pagès, et al. Unconstrained recursive importance sampling. The Annals of Applied Probability, 20(3):1029–1067, 2010.
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