Simulation
Use SDE model objects with functions for standard Monte Carlo simulations and quasi-Monte Carlo simulations.
Objects
sde | Stochastic Differential Equation (SDE ) model |
bm | Brownian motion (BM ) models |
gbm | Geometric Brownian motion (GBM ) model |
merton |
Merton jump diffusion model (Since R2020a) |
bates |
Bates stochastic volatility model (Since R2020a) |
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 |
rvm | Rough volatility model (RVM ) (Since R2023b) |
roughbergomi | Rough Bergomi model (Since R2024a) |
roughheston | Rough Heston model (Since R2024b) |
Functions
Topics
- Simulating Equity Prices
This example compares alternative implementations of a separable multivariate geometric Brownian motion process.
- Simulating Interest Rates
This example highlights the flexibility of refined interpolation by implementing this power-of-two algorithm.
- Stratified Sampling
This example specifies a noise function to stratify the terminal value of a univariate equity price series.
- Price American Basket Options Using Standard Monte Carlo and Quasi-Monte Carlo Simulation
Model the fat-tailed behavior of asset returns and assess the impact of alternative joint distributions on basket option prices.
- Improving Performance of Monte Carlo Simulation with Parallel Computing
This example shows how to improve the performance of a Monte Carlo simulation using Parallel Computing Toolbox™.
- SDEs
Model dependent financial and economic variables by performing standard Monte Carlo or Quasi-Monte Carlo simulation of stochastic differential equations (SDEs).
- 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.
- Performance Considerations
Performance considerations for managing memory when solving most problems supported by the SDE engine.