SDE with Linear Drift model

Creates and displays SDE objects whose drift rate is expressed in linear
drift-rate form and that derive from the `sdeddo`

(SDE from drift and diffusion objects class).

Use `sdeld`

objects to simulate sample paths of
`NVARS`

state variables expressed in linear drift-rate form. They
provide a parametric alternative to the mean-reverting drift form (see `sdemrd`

).

These state variables are driven by `NBROWNS`

Brownian motion sources
of risk over `NPERIODS`

consecutive observation periods, approximating
continuous-time stochastic processes with linear drift-rate functions.

The `sdeld`

object allows you to simulate any vector-valued SDELD of
the form:

$$d{X}_{t}=(A(t)+B(t){X}_{t})dt+D(t,{X}_{t}^{\alpha (t)})V(t)d{W}_{t}$$

where:

*X*is an_{t}`NVARS`

-by-`1`

state vector of process variables.*A*is an`NVARS`

-by-`1`

vector.*B*is an`NVARS`

-by-`NVARS`

matrix.*D*is an`NVARS`

-by-`NVARS`

diagonal matrix, where each element along the main diagonal is the corresponding element of the state vector raised to the corresponding power of*α*.*V*is an`NVARS`

-by-`NBROWNS`

instantaneous volatility rate matrix.*dW*is an_{t}`NBROWNS`

-by-`1`

Brownian motion vector.

creates a `SDELD`

= sdeld(___,`Name,Value`

)`SDELD`

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 `SDELD`

object has the following displayed 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`A`

— Access function for the input argument`A`

, callable as a function of time and state`B`

— Access function for the input argument`B`

, callable as a function of time and state`Alpha`

— Access function for the input argument`Alpha`

, callable as a function of time and state`Sigma`

— Access function for the input argument`Sigma`

, 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,

`sdeld`

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

[2] Ait-Sahalia, Y. “Transition Densities for Interest Rate and Other
Nonlinear Diffusions.” *The Journal of Finance*, Vol. 54,
No. 4, August 1999.

[3] Glasserman, P. *Monte Carlo Methods in Financial
Engineering.* New York, Springer-Verlag, 2004.

[4] Hull, J. C. *Options, Futures, and Other Derivatives*, 5th
ed. Englewood Cliffs, NJ: Prentice Hall, 2002.

[5] Johnson, N. L., S. Kotz, and N. Balakrishnan. *Continuous Univariate
Distributions.* Vol. 2, 2nd ed. New York, John Wiley & Sons,
1995.

[6] Shreve, S. E. *Stochastic Calculus for Finance II: Continuous-Time
Models.* New York: Springer-Verlag, 2004.

`diffusion`

| `drift`

| `nearcorr`

| `sdeddo`

| `simByEuler`

- Linear Drift Models
- Implementing Multidimensional Equity Market Models, Implementation 3: Using SDELD, CEV, and GBM Objects
- Simulating Equity Prices
- Simulating Interest Rates
- Stratified Sampling
- Pricing American Basket Options by Monte Carlo Simulation
- Base SDE Models
- Drift and Diffusion Models
- Linear Drift Models
- Parametric Models
- SDEs
- SDE Models
- SDE Class Hierarchy
- Performance Considerations