"""Cox-Ingersoll-Ross process."""
from stochastic.processes.diffusion.diffusion import DiffusionProcess
from stochastic.utils import single_arg_constant_function
[docs]class CoxIngersollRossProcess(DiffusionProcess):
r"""Cox-Ingersoll-Ross process.
.. image:: _static/cox_ingersoll_ross_process.png
:scale: 50%
A model for instantaneous interest rate.
The process :math:`X_t` that satisfies the following stochastic
differential equation with Wiener process :math:`W_t`:
.. math::
dX_t = \theta (\mu - X_t) dt + \sigma \sqrt{X_t} dW_t
Realizations are generated using the Euler-Maruyama method.
.. note::
Since the family of diffusion processes have parameters which
generalize to functions of ``t``, parameter attributes will be returned
as callables, even if they are initialized as constants. e.g. a
``speed`` parameter of 1 accessed from an instance attribute will return
a function which accepts a single argument and always returns 1.
:param float speed: the speed of reversion, or :math:`\theta` above
:param float mean: the mean of the process, or :math:`\mu` above
:param float vol: volatility coefficient of the process, or :math:`\sigma`
above
:param float t: the right hand endpoint of the time interval :math:`[0,t]`
for the process
:param numpy.random.Generator rng: a custom random number generator
"""
def __init__(self, speed=1, mean=0, vol=1, t=1, rng=None):
super().__init__(
speed=single_arg_constant_function(speed),
mean=single_arg_constant_function(mean),
vol=single_arg_constant_function(vol),
volexp=single_arg_constant_function(0.5),
t=t,
rng=rng,
)
def __str__(self):
return "Cox-Ingersoll-Ross process with speed={s}, mean={m}, vol={v} on [0, {t}]".format(
s=str(self.speed), m=str(self.mean), v=str(self.vol), t=str(self.t)
)
def __repr__(self):
return "CoxIngersollRossProcess(speed={s}, mean={m}, vol={v}, t={t})".format(
s=str(self.speed), m=str(self.mean), v=str(self.vol), t=str(self.t)
)