Variance-Gamma Process#

Simulate and visualise paths

# Author: Dialid Santiago <d.santiago@outlook.com>
# License: MIT
# Description: Simulate and visualise a Variance-Gamma Process

from aleatory.processes import VarianceGammaProcess
from aleatory.styles import qp_style

qp_style()  # Use quant-pastel-style

p = VarianceGammaProcess()
fig = p.draw(n=100, N=200, figsize=(12, 7), colormap="winter")
fig.show()

$Variance Gamma Process X($\theta$=0.0, $\nu$=1.0, $\sigma$=1.0), Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal$

p = VarianceGammaProcess(theta=-1.0, nu=4.0, sigma=2.0, T=100.0)
fig = p.draw(n=100, N=200, figsize=(12, 7), colormap="summer")
fig.show()

$Variance Gamma Process X($\theta$=-1.0, $\nu$=4.0, $\sigma$=2.0), Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal$

fig = p.plot(n=200, N=10, figsize=(12, 7))
fig.show()

$Variance Gamma Process X($\theta$=-1.0, $\nu$=4.0, $\sigma$=2.0)$

Total running time of the script: (0 minutes 3.092 seconds)

Gallery generated by Sphinx-Gallery