2.5.6. sdepy.cox_ingersoll_ross_process

class sdepy.cox_ingersoll_ross_process(paths=1, vshape=(), dtype=None, steps=None, i0=0, info=None, getinfo=True, method='euler', x0=1., theta=1., k=1., xi=1., dw=None, corr=None, rho=None)[source]

Cox-Ingersoll-Ross mean reverting process.

Generates a process x(t) that solves the following SDE:

dx(t) = k(t)*(theta(t) - x(t))*dt + xi(t)*sqrt(x(t))*dw(t, dt)

where dw(t, dt) are standard Wiener process increments with correlation matrix specified by corr(t) or rho(t). x0, SDE parameters and dw(t, dt) should broadcast to vshape + (paths,). x0, theta, k should be positive.

Parameters:
paths, vshape, dtype, steps, i0, info, getinfo, method

See SDE class documentation.

x0 : array-like

Initial condition.

theta, k, xi : array-like, or callable

SDE parameters.

dw, corr, rho

Specification of stochasticity source of Wiener process increments. See SDE.source_dw documentation.

Returns:
x : process

Once instantiated as p, p(timeline) performs the integration along the given timeline, based on parameters of instantiation, and returns the resulting process.

Attributes:
See SDE class documentation.

Methods

See SDE class documentation.