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Create SDE models


sdeStochastic Differential Equation (SDE) model
bates Bates stochastic volatility model
bmBrownian motion (BM) models
gbmGeometric Brownian motion (GBM) model
merton Merton jump diffusion model
driftDrift-rate model component
diffusionDiffusion-rate model component
sdeddoStochastic Differential Equation (SDEDDO) model from Drift and Diffusion components
sdeldSDE with Linear Drift (SDELD) model
cevConstant Elasticity of Variance (CEV) model
cirCox-Ingersoll-Ross (CIR) mean-reverting square root diffusion model
hestonHeston model
hwvHull-White/Vasicek (HWV) Gaussian Diffusion model
sdemrdSDE 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.


  • 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.