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ajdmom: A Python Package for Deriving Moment Formulas of Affine Jump Diffusion Processes

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  • Yan-Feng Wu
  • Jian-Qiang Hu

Abstract

We introduce ajdmom, a Python package designed for automatically deriving moment formulae for the well-established affine jump diffusion processes with state-independent jump intensities. ajdmom can produce explicit closed-form expressions for conditional and unconditional moments of any order, significantly enhancing the usability of these models. Additionally, ajdmom can compute partial derivatives of these moments with respect to the model parameters, offering a valuable tool for sensitivity analysis. The package's modular architecture makes it easy for adaptation and extension by researchers. ajdmom is open-source and readily available for installation from GitHub or the Python package index (PyPI).

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  • Yan-Feng Wu & Jian-Qiang Hu, 2024. "ajdmom: A Python Package for Deriving Moment Formulas of Affine Jump Diffusion Processes," Papers 2411.06484, arXiv.org, revised Apr 2025.
    • Handle: RePEc:arx:papers:2411.06484
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    1. Canyakmaz, Caner & Özekici, Süleyman & Karaesmen, Fikri, 2019. "An inventory model where customer demand is dependent on a stochastic price process," International Journal of Production Economics, Elsevier, vol. 212(C), pages 139-152.
    2. Jiang, George J & Knight, John L, 2002. "Estimation of Continuous-Time Processes via the Empirical Characteristic Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 198-212, April.
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    Cited by:

    1. Yan-Feng Wu & Jian-Qiang Hu, 2025. "Density Approximation of Affine Jump Diffusions via Closed-Form Moment Matching," Papers 2504.06942, arXiv.org.

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