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Econometrics Toolbox™ software enables you to model dependent financial and economic variables, such as interest rates and equity prices, by performing 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 | |

Performance Considerations | All supported models | |

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 usually 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*.

SDE methods in the Econometrics 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.

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