# Specification

Create SDE models

## Objects

 `sde` Stochastic Differential Equation (`SDE`) model `bates` `Bates` stochastic volatility model `bm` Brownian motion (`BM`) models `gbm` Geometric Brownian motion (`GBM`) model `merton` `Merton` jump diffusion model `drift` Drift-rate model component `diffusion` Diffusion-rate model component `sdeddo` Stochastic Differential Equation (`SDEDDO`) model from Drift and Diffusion components `sdeld` SDE with Linear Drift (`SDELD`) model `cev` Constant Elasticity of Variance (`CEV`) model `cir` Cox-Ingersoll-Ross (`CIR`) mean-reverting square root diffusion model `heston` `Heston` model `hwv` Hull-White/Vasicek (`HWV`) Gaussian Diffusion model `sdemrd` SDE with Mean-Reverting Drift (`SDEMRD`) model

## Examples and How To

• Base SDE Models

Use base SDE models to represent a univariate geometric Brownian Motion model.

• Drift and Diffusion Models

Create `SDE` objects with combinations of customized drift or diffusion functions and objects.

• Linear Drift Models

`sdeld` objects provide a parametric alternative to the mean-reverting drift form.

• Parametric Models

Financial Toolbox™ supports several parametric models based on the SDE class hierarchy.

## Concepts

• SDEs

Model dependent financial and economic variables by performing standard Monte Carlo or Quasi-Monte Carlo simulation of stochastic differential equations (SDEs).

• SDE Class Hierarchy

The SDE class structure represents a generalization and specialization hierarchy.

• SDE Models

Most models and utilities available with Monte Carlo Simulation of SDEs are represented as MATLAB® objects.

• Quasi-Monte Carlo Simulation

Quasi-Monte Carlo simulation is a Monte Carlo simulation but uses quasi-random sequences instead pseudo random numbers.