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A general machine learning framework of real-time evaluation for financial derivatives portfolios

Author

Listed:
  • Liangliang Zhang

    (Shanghai Liangbai Technologies Co., Ltd)

  • Ruyan Tian

    (Fudan University)

  • Qing Yang

    (Fudan University)

  • Tingting Ye

    (University of Maine)

Abstract

In this paper, we develop efficient numerical methods and a general framework, based on AI technologies, to solve for financial derivatives prices and Greeks in a real-time manner. Our methodologies extend the traditional path derivative, likelihood ratio and finite difference approaches, making full use of machine learning techniques, and are able to produce fast and accurate estimates of financial derivative prices and risk-factor sensitivity metrics such as Delta, Gamma, Rho and Vega. The machine learning based computational framework proposed is of both theoretical and practical interest and is readily applicable to day-to-day business. Moreover, we propose a state-of-the-art way for model parameter inference.

Suggested Citation

  • Liangliang Zhang & Ruyan Tian & Qing Yang & Tingting Ye, 2025. "A general machine learning framework of real-time evaluation for financial derivatives portfolios," Review of Derivatives Research, Springer, vol. 28(2), pages 1-21, July.
    • Handle: RePEc:kap:revdev:v:28:y:2025:i:2:d:10.1007_s11147-025-09216-5
    DOI: 10.1007/s11147-025-09216-5
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    References listed on IDEAS

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    More about this item

    Keywords

    Real-time derivatives pricing; Machine learning; Path derivative method; Likelihood ratio; Malliavin weighting; Monte Carlo finite difference; Risk neutral no arbitrage pricing; Greeks; Regression pricing; Least-square Monte Carlo;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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