Define a Wiener process with drift and diffusion from the stochastic differential equation (SDE) :

Directly convert from the parametric process:

Define a process , where :

Use differential notation to define the same process:

Define a vector process with output :

Using differential notation:

Define a vector process , where :

Using differential notation:

Define a vector process where :

Using differential notation:

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:

Simulate ItoProcess paths using different methods:

Simulation methods and their corresponding orders:

Specify the simulation method as an option in RandomFunction:

Define an Ito process by its stochastic differential equation:

Available Ito process properties:

Drift and diffusion of the process:

Kolmogorov forward equation:

Inactive is used here to avoid expanding the partial derivatives; use Activate to expand the expression:

Kolmogorov backward equation:

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:

Drift:

Diffusion matrix (after canonicalization):

Kolmogorov forward equation:

Kolmogorov backward equation:

Ito derivative formula:

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:

Define Jacobi diffusion process:

Compute low-order 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:

Compute cross‐covariance of and :

Infinite time horizon limit exists only if :

Compute cross-covariance 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):

Simulate process paths:

Compute mean and variance functions:

Plot mean function and the standard deviation band, together with generated paths:

Determine values of and for which the process is a martingale, where is the standard Wiener process:

Convert to the standard form:

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:

Quadratic variation of :

By Lévy characterization, is a Brownian motion. The mean of the process is the same as the initial state:

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 :

Visualize the sampled path:

Generate a thousand samples with the same conditions, then visualize the paths and slice data at :

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 closed-form density function:

Visualize the dynamic of the solution with Animate:

Represent a GeometricBrownianMotionProcess with ItoProcess, where represents the risk-free 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: