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Enhancing path-integral approximation for non-linear diffusion with neural network

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  • Anna Knezevic

Abstract

Enhancing the existing solution for pricing of fixed income instruments within Black-Karasinski model structure, with neural network at various parameterisation points to demonstrate that the method is able to achieve superior outcomes for multiple calibrations across extended projection horizons.

Suggested Citation

  • Anna Knezevic, 2024. "Enhancing path-integral approximation for non-linear diffusion with neural network," Papers 2404.08903, arXiv.org.
    • Handle: RePEc:arx:papers:2404.08903
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    References listed on IDEAS

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    3. Eckhard Platen, 1999. "An Introduction to Numerical Methods for Stochastic Differential Equations," Research Paper Series 6, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. Andrzej Daniluk & Rafał Muchorski, 2016. "Approximations Of Bond And Swaption Prices In A Black–Karasiński Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-32, May.
    5. Realdon, Marco, 2016. "Tests of non linear Gaussian term structure models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 128-147.
    6. C. Turfus, 2019. "Closed-form Arrow-Debreu pricing for the Hull-White short rate model," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2087-2094, December.
    7. Ho, Teng-Suan & Stapleton, Richard C & Subrahmanyam, Marti G, 1995. "Multivariate Binomial Approximations for Asset Prices with Nonstationary Variance and Covariance Characteristics," The Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 1125-1152.
    8. Luca Capriotti, 2007. "A Closed-Form Approximation of Likelihood Functions for Discretely Sampled Diffusions: the Exponent Expansion," Papers physics/0703180, arXiv.org.
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