ajdmom: A Python Package for Deriving Moment Formulas of Affine Jump Diffusion Processes
Yan-Feng Wu, Jian-Qiang Hu

TL;DR
ajdmom is a Python package that automatically derives explicit moment formulas and their derivatives for affine jump diffusion processes, facilitating model analysis and sensitivity studies.
Contribution
It introduces a modular, open-source tool that simplifies deriving moments and derivatives for affine jump diffusion models, enhancing research efficiency.
Findings
Produces explicit closed-form moment formulas
Computes derivatives of moments with respect to parameters
Enhances usability of affine jump diffusion models
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).
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Mathematical Modeling in Engineering
MethodsDiffusion
