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jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets
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DOI：10.18637/jss.v105.i04.png)
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Abstract
En 中文
We introduce a Python library, called jumpdiff, which includes all necessary func-tions to assess jump-diffusion processes. This library includes functions which compute a set of non-parametric estimators of all contributions composing a jump-diffusion pro-cess, namely the drift, the diffusion, and the stochastic jump strengths. Having a set of measurements from a jump-diffusion process, jumpdiff is able to retrieve the evolution equation producing data series statistically equivalent to the series of measurements. The back-end calculations are based on second-order corrections of the conditional moments expressed from the series of Kramers-Moyal coefficients. Additionally, the library is also able to test if stochastic jump contributions are present in the dynamics underlying a set of measurements. Finally, we introduce a simple iterative method for deriving second -order corrections of any Kramers-Moyal coefficient.
Keywords:
stochastic differential equations
jump-diffusion processes
Kramers-Moyal expan-sion
Kramers-Moyal coefficients
Python
Journal
IF:
8.1
Papers:
622
Citations:
4.6W