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Create SDE models

Construct SDE model objects.


sdeStochastic Differential Equation (SDE) model
bates Bates stochastic volatility model (Since R2020a)
bmBrownian motion (BM) models
gbmGeometric Brownian motion (GBM) model
merton Merton jump diffusion model (Since R2020a)
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
rvmRough volatility model (RVM) (Since R2023b)
roughbergomiRough Bergomi model (Since R2024a)


  • 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.