Jump-Diffusion Parameter Estimation
A Python library for simulating and estimating parameters of jump-diffusion processes, with pluggable jump-size distributions and goodness-of-fit comparison tools.
The model follows the stochastic differential equation:
\[dX_t = \mu\, dt + \sigma\, dW_t + J_t\, dN_t\]
where \(\mu\) is the drift, \(\sigma\) is the diffusion volatility,
\(W_t\) is a standard Brownian motion, \(J_t\) is the jump size
(drawn from a pluggable JumpDistribution),
and \(N_t\) is a jump-arrival process approximated by Bernoulli trials.