|Stochastic Differential Equation (SDE) model|
|Brownian motion models|
|Geometric Brownian motion model|
|Merton jump diffusion model|
|Bates stochastic volatility model|
|Drift-rate model component|
|Diffusion-rate model component|
|Stochastic Differential Equation (SDE) model from Drift and Diffusion components|
|SDE with Linear Drift model|
|Constant Elasticity of Variance (CEV) model|
|Cox-Ingersoll-Ross mean-reverting square root diffusion model|
|Hull-White/Vasicek Gaussian Diffusion model|
|SDE with Mean-Reverting Drift model|
For sde, bm, gbm, cev, cir, hwv, heston, sdeddo, sdeld, or sdemrd models
For heston models
For cir models
For gbm models
For hwv models
For bates models
For merton models
Conversion for time series arrays to functions of time and state
Examples and How To
This example compares alternative implementations of a separable multivariate geometric Brownian motion process.
This example highlights the flexibility of refined interpolation by implementing this power-of-two algorithm.
This example specifies a noise function to stratify the terminal value of a univariate equity price series.
This example shows how to model the fat-tailed behavior of asset returns and assess the impact of alternative joint distributions on basket option prices.
This example shows how to improve the performance of a Monte Carlo simulation using Parallel Computing Toolbox™.
Model dependent financial and economic variables by performing Monte Carlo simulation of stochastic differential equations (SDEs).
Most models and utilities available with Monte Carlo Simulation of SDEs are represented as MATLAB® objects.
Performance considerations for managing memory when solving most problems supported by the SDE engine.