Stochastic Differential Equation (SDE) model

Creates and displays general stochastic differential equation
(`SDE`

) models from user-defined drift and diffusion rate
functions.

Use `sde`

objects to simulate sample paths of
`NVars`

state variables driven by `NBROWNS`

Brownian motion sources of risk over `NPeriods`

consecutive observation
periods, approximating continuous-time stochastic processes.

An `sde`

object enables you to simulate any vector-valued SDE of the form:

$$d{X}_{t}=F(t,{X}_{t})dt+G(t,{X}_{t})d{W}_{t}$$

where:

*X*is an_{t}`NVars`

-by-`1`

state vector of process variables.*dW*is an_{t}`NBROWNS`

-by-`1`

Brownian motion vector.*F*is an`NVars`

-by-`1`

vector-valued drift-rate function.*G*is an`NVars`

-by-`NBROWNS`

matrix-valued diffusion-rate function.

creates a default `SDE`

= sde(`DriftRate`

,`DiffusionRate`

)`SDE`

object.

creates a `SDE`

= sde(___,`Name,Value`

)`SDE`

object with additional options specified by
one or more `Name,Value`

pair arguments.

`Name`

is a property name and `Value`

is
its corresponding value. `Name`

must appear inside single
quotes (`''`

). You can specify several name-value pair
arguments in any order as
`Name1,Value1,…,NameN,ValueN`

.

The `SDE`

object has the following Properties:

`StartTime`

— Initial observation time`StartState`

— Initial state at time`StartTime`

`Correlation`

— Access function for the`Correlation`

input argument, callable as a function of time`Drift`

— Composite drift-rate function, callable as a function of time and state`Diffusion`

— Composite diffusion-rate function, callable as a function of time and state`Simulation`

— A simulation function or method

`interpolate` | Brownian interpolation of stochastic differential equations |

`simulate` | Simulate multivariate stochastic differential equations (SDEs) |

`simByEuler` | Euler simulation of stochastic differential equations (SDEs) |

When you specify the required input parameters as arrays, they are associated with a specific parametric form. By contrast, when you specify either required input parameter as a function, you can customize virtually any specification.

Accessing the output parameters with no inputs simply returns the original input specification. Thus, when you invoke these parameters with no inputs, they behave like simple properties and allow you to test the data type (double vs. function, or equivalently, static vs. dynamic) of the original input specification. This is useful for validating and designing methods.

When you invoke these parameters with inputs, they behave like functions, giving the
impression of dynamic behavior. The parameters accept the observation time
*t* and a state vector
*X _{t}*, and return an array of appropriate
dimension. Even if you originally specified an input as an array,

`sde`

treats it as a static function of time and state, by that means guaranteeing that all
parameters are accessible by the same interface.[1] Aït-Sahalia, Yacine. “Testing
Continuous-Time Models of the Spot Interest Rate.” *Review of Financial
Studies*, vol. 9, no. 2, Apr. 1996, pp. 385–426.

[2] Aït-Sahalia, Yacine.
“Transition Densities for Interest Rate and Other Nonlinear Diffusions.” *The
Journal of Finance*, vol. 54, no. 4, Aug. 1999, pp.
1361–95.

[3] Glasserman, Paul.
*Monte Carlo Methods in Financial Engineering*. Springer,
2004.

[4] Hull, John.
*Options, Futures and Other Derivatives*. 7th ed, Prentice
Hall, 2009.

[5] Johnson, Norman Lloyd, et al.
*Continuous Univariate Distributions*. 2nd ed, Wiley,
1994.

[6] Shreve, Steven E.
*Stochastic Calculus for Finance*. Springer,
2004.

- Base SDE Models
- Representing Market Models Using SDE Objects
- Simulating Equity Prices
- Simulating Interest Rates
- Stratified Sampling
- Pricing American Basket Options by Monte Carlo Simulation
- Drift and Diffusion Models
- Linear Drift Models
- Parametric Models
- SDEs
- SDE Models
- SDE Class Hierarchy
- Performance Considerations