## SDEs

### SDE Modeling

Financial Toolbox™ enables you to model dependent financial and economic variables, such as interest rates and equity prices, by performing standard Monte Carlo or Quasi-Monte Carlo simulation of stochastic differential equations (SDEs). The flexible architecture of the SDE engine provides efficient simulation methods that allow you to create new simulation and derivative pricing methods.

The following table lists tasks you can perform using the SDE functionality.

To perform this task ... | Use these types of models ... |
---|---|

Simulating Equity Prices | |

Simulating Interest Rates | |

Pricing Equity Options | |

Stratified Sampling | All supported models |

Quasi-Monte Carlo Simulation | All supported models |

Performance Considerations | All supported models |

### Trials vs. Paths

Monte Carlo simulation literature often uses different terminology for the
evolution of the simulated variables of interest, such as
*trials* and *paths*. The following
sections use the terms *trial* and *path*
interchangeably.

However, there are situations where you should distinguish between these terms.
Specifically, the term *trial* often implies the result of an
independent random experiment (for example, the evolution of the price of a single
stock or portfolio of stocks). Such an experiment computes the average or expected
value of a variable of interest (for example, the price of a derivative security)
and its associated confidence interval.

By contrast, the term *path* implies the result of a random
experiment that is different or unique from other results, but that may or may not
be independent.

The distinction between these terms is unimportant. It may, however, be useful
when applied to *variance reduction* techniques that attempt to
increase the efficiency of Monte Carlo simulation by inducing dependence across
sample paths. A classic example involves pairwise dependence induced by
*antithetic sampling*, and applies to more sophisticated
variance reduction techniques, such as *stratified sampling*
which is a variance reduction technique that constrains a proportion of sample paths
to specific subsets (or *strata*) of the sample space.

### NTrials, NPeriods, and NSteps

SDE functions in the Financial Toolbox software use the parameters `NTrials`

,
`NPeriods`

, and `NSteps`

as follows:

The input argument

`NTrials`

specifies the number of simulated trials or sample paths to generate. This argument always determines the size of the third dimension (the number of pages) of the output three-dimensional time series array`Paths`

. Indeed, in a traditional Monte Carlo simulation of one or more variables, each sample path is independent and represents an independent trial.The parameters

`NPeriods`

and`NSteps`

represent the number of simulation periods and time steps, respectively. Both periods and time steps are related to time increments that determine the exact sequence of observed sample times. The distinction between these terms applies only to issues of accuracy and memory management. For more information, see Optimizing Accuracy: About Solution Precision and Error and Managing Memory.

## See Also

`sde`

| `bm`

| `gbm`

| `bates`

| `merton`

| `drift`

| `diffusion`

| `sdeddo`

| `sdeld`

| `cev`

| `cir`

| `heston`

| `hwv`

| `sdemrd`

| `rvm`

| `roughbergomi`

| `ts2func`

| `simulate`

| `simByQuadExp`

| `simByEuler`

| `simBySolution`

| ` | `

`interpolate`