ItoProcess
ItoProcess[{a,b},x,t]
represents an Ito process , where .
ItoProcess[{a,b,c},x,t]
represents an Ito process , where .
ItoProcess[…,{x,x_{0}},{t,t_{0}}]
uses initial condition .
ItoProcess[…,…,…,Σ]
uses a Wiener process , with covariance Σ.
ItoProcess[proc]
converts proc to a standard Ito process whenever possible.
ItoProcess[sdeqns,expr,x,t,wdproc]
represents an Ito process specified by a stochastic differential equation sdeqns, output expression expr, with state x and time t, driven by w following the process dproc.
Details and Options
 ItoProcess is also known as Ito diffusion or stochastic differential equation (SDE).
 ItoProcess is a continuoustime and continuousstate random process.
 If the drift a is an dimensional vector and the diffusion b an ×dimensional matrix, the process is dimensional and driven by an dimensional WienerProcess.
 Common specifications for coefficients a and b include:

a scalar, b scalar a scalar, b vector a vector, b vector a vector, b matrix  A stochastic differential equation is sometimes written as an integral equation .
 The default initial time t_{0} is taken to be zero, and the default initial state x_{0} is zero.
 The default covariance Σ is the identity matrix.
 For a general covariance Σ, ItoProcess canonicalizes the process by converting the diffusion matrix b to b.Σ^{1/2}, with Σ^{1/2} the lower Cholesky factor of Σ when possible. »
 A standard Ito process has output , consisting of a subset of differential states .
 Processes proc that can be converted to standard ItoProcess form include OrnsteinUhlenbeckProcess, GeometricBrownianMotionProcess, StratonovichProcess, and ItoProcess.
 Converting an ItoProcess to standard form automatically makes use of Ito's lemma.
 The stochastic differential equations in sdeqns can be of the form , where is \[DifferentialD], which can be input using dd. The differentials and are taken to be Ito differentials.
 The output expression expr can be any expression involving x[t] and t.
 The driving process dproc can be any process that can be converted to a standard Ito process.
 Properties related to ItoProcess include:

"Drift" drift term "Diffusion" diffusion matrix "Output" output state "TimeVariable" time variable "TimeOrigin" origin of time variable "StateVariables" state variables "InitialState" initial state values "KolmogorovForwardEquation" Kolmogorov forward equation (FokkerPlanck equation) "KolmogorovBackwardEquation" Kolmogorov backward equation "Derivative" Ito derivative "FeynmanKacFormula" PDE obtained from FeynmanKac formula  Method settings in RandomFunction specific to ItoProcess include:

"EulerMaruyama" Euler–Maruyama (order 1/2, default) "KloedenPlatenSchurz" Kloeden–Platen–Schurz (order 3/2) "Milstein" Milstein (order 1) "StochasticRungeKutta" 3‐stage Rossler SRK scheme (order 1) "StochasticRungeKuttaScalarNoise" 3‐stage Rossler SRK scheme for scalar noise (order 3/2)  ItoProcess can be used with such functions as RandomFunction, CovarianceFunction, PDF, and Expectation.
Examples
open allclose allBasic Examples (1)
Scope (18)
Basic Uses (9)
Define a Wiener process with drift and diffusion from the stochastic differential equation (SDE) :
Directly convert from the parametric process:
Use differential notation to define the same process:
Define a vector process with output :
Define a vector process , where :
Define a vector process where :
Define a process driven by two correlated Wiener processes:
The canonicalized process has diffusion matrix equal to , with the diffusion matrix before canonicalization:
Define a scalar process corresponding to the SDE :
Define vector process and corresponding to the SDE and :
Define a process corresponding to the 2D correlated Wiener process:
Define vector process driven by correlated 2D Wiener process:
Process Properties Extraction (2)
Define an Ito process by its stochastic differential equation:
Available Ito process properties:
Drift and diffusion of the process:
Inactive is used here to avoid expanding the partial derivatives; use Activate to expand the expression:
Compute the Ito derivative of a function . The output is a list consisting of drift and diffusion terms:
The property "FeynmanKacFormula" gives a PDE whose solution satisfies the conditional expectation and terminal condition :
Additional arguments can be provided for the generalized situations. With an additional argument , the property "FeynmanKacFormula" gives a PDE whose solution satisfies the conditional expectation and the same terminal condition:
With a third argument , the property "FeynmanKacFormula" gives a PDE whose solution satisfies the conditional expectation and the same terminal condition:
Define Heston model with ItoProcess:
Special Ito Processes (5)
An Ito process corresponding to the WienerProcess:
An Ito process corresponding to the GeometricBrownianMotionProcess:
An Ito process corresponding to the BrownianBridgeProcess:
An Ito process corresponding to the OrnsteinUhlenbeckProcess:
An Ito process corresponding to the CoxIngersollRossProcess:
Process Slice Properties (2)
Define Jacobi diffusion process:
Compute loworder cumulants of time‐slice distribution:
Find the limit of infinite time horizon:
Compare with cumulants of the uniform distribution:
Define a vector process given by a system of linear SDEs:
Find the probability density function of the time‐slice distribution:
Applications (11)
Computing Properties (3)
Compute crosscovariance of the Ornstein–Uhlenbeck process and its underlying Wiener process :
Compute moments of the process , where is the standard Wiener process:
Vector Ito process driven by scalar noise (1D oscillator driven by white noise):
Compute mean and variance functions:
Plot mean function and the standard deviation band, together with generated paths:
Martingales (3)
Determine values of and for which the process is a martingale, where is the standard Wiener process:
Zero drift coefficient of the standard form is a necessary condition for to be a martingale:
Scalar Ito process driven by vector Wiener process:
Define the same process via a stochastic equation:
Construct a scalar process driven by two Wiener processes:
By Lévy characterization, is a Brownian motion. The mean of the process is the same as the initial state:
Modeling (2)
The dynamics of a free particle under the effect of thermal fluctuation can be modeled by the Langevin equation of motion, , where is the standard WienerProcess and is the strength of the thermal noise. Here it is assumed that can only depend on and focus on the equation of velocity. There are two common ways to integrate the equation of motion: Ito formulation and Stratonovich formulation. They can be defined via:
When is a constant, the two formulations are identical and lead to the same stationary distribution as :
If is velocity dependent, then due to the nature of the WienerProcess, has nonzero quadratic variation and the two formulations lead to different results. Convert Stratonovich formulation to the equivalent Ito formulation:
The drift under Stratonovich formulation is different from the drift under Ito formulation:
The Gompertz curve is typically used in the modeling of a growth process, such as tumor growth. By assuming Gaussian noise in the logarithm of the growth process, you can write the model as a stochastic differential equation:
Mean of the process is the usual Gompertz curve:
Slice distribution of the process at time obeys LogNormalDistribution:
Simulate the process with , and from to :
Generate a thousand samples with the same conditions, then visualize the paths and slice data at :
Ito Process Representations (3)
Use ItoProcess to represent the standard WienerProcess:
is a martingale. Use Ito lemma to compute the derivative of :
Create a CoxIngersollRossProcess and represent it with ItoProcess:
Obtain Kolmogorov forward equation:
Solve the equation numerically in with a localized initial condition at and Dirichlet boundary conditions:
Plot the solution of Kolmogorov forward equation at and compare it with the closedform density function:
Visualize the dynamic of the solution with Animate:
Represent a GeometricBrownianMotionProcess with ItoProcess, where represents the riskfree interest rate and is the volatility:
Compute Ito differential for a discounted process :
The classical Black–Scholes equation can be obtained by equating the drift to 0 with some simplifications:
The equation can be also obtained directly from the Feynman–Kac formula:
Solve the Black–Scholes equation symbolically with DSolve:
Properties & Relations (2)
Convert StratonovichProcess to ItoProcess:
Transformed Wiener processes are related to ItoProcess:
Possible Issues (2)
ItoProcess does not support random initial conditions, so cannot be represented:
But it supports processes with fixed initial condition:
Initial time of the driven process needs to match with ItoProcess:
Text
Wolfram Research (2012), ItoProcess, Wolfram Language function, https://reference.wolfram.com/language/ref/ItoProcess.html (updated 2016).
CMS
Wolfram Language. 2012. "ItoProcess." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2016. https://reference.wolfram.com/language/ref/ItoProcess.html.
APA
Wolfram Language. (2012). ItoProcess. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ItoProcess.html