sim.gbm: Simulate paths of Geometric Brownian Motion with constant...
In mludkov/mlOSP: Machine Learning and Regression Monte Carlo Algorithms for Optimal Stopping
sim.gbm R Documentation
Simulate paths of Geometric Brownian Motion with constant parameters
Description
Simulate paths of Geometric Brownian Motion with constant parameters
Usage
sim.gbm(x0, model, dt = model$dt)
Arguments
x0
is the starting values (matrix of size N x model$dim)
model
a list containing all the other parameters, including volatility model$sigma,
interest rate model$r and continuous dividend yield model$div.
dt
is the step size in time. Defaults to model$dt
Details
Simulate from p(X_t|X_{t-1}).
Use log-normal transition density specified by the model parameters
Value
a vector of same dimensions as x0
mludkov/mlOSP documentation built on April 29, 2023, 7:56 p.m.
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| sim.gbm | R Documentation |
Simulate paths of Geometric Brownian Motion with constant parameters
Description
Simulate paths of Geometric Brownian Motion with constant parameters
Usage
sim.gbm(x0, model, dt = model$dt)
Arguments
x0 |
is the starting values (matrix of size N x model$dim) |
model |
a list containing all the other parameters, including volatility |
dt |
is the step size in time. Defaults to |
Details
Simulate from p(X_t|X_{t-1}).
Use log-normal transition density specified by the model parameters
Value
a vector of same dimensions as x0
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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