Brownian package is aimed at providing a centralized repository of algorithms for simulating Brownian-based
stochastic processes. More precisely, the package currently provides routines for random sampling from
The future roadmap would be to provide implementations for sampling from
Willing developers are welcome to contribute to the package.
To simulate Brownian motion at the time points (0, 0.1, 0.5, 0.75, 1), use the following snippet:
using Brownian p = BrownianMotion([0, 0.1, 0.5, 0.75, 1]) rand(p)
Suppose that interest is in simulating fractional Brownian motion with Hurst index equal to 0.4 in the time interval [0, 1] with a time step of 1/2^n for some natural n (for example n=10).
using Brownian p = FBM(0:1/2^10:1, 0.4) # Using the Davies-Harte algorithm, which relies on fast Fourier transforms (FFT) rand(p) # Using a method based on the Cholesky decomposition of the covariance matrix of FBM rand(p, method=:chol) # Using an exact discrete method for simulating Riemann-Liouville FBM rand(p, method=:rl)
To simulate fractional Gaussian noise with the same Hurst index,
# Using the Davies-Harte algorithm rand(p, fbm=false) # Using the Cholesky method rand(p, fbm=false, method=:chol)
Note that fractional Brownian motion is obtained from fractional Gaussian noise by taking cumulative sums (and conversely FGN is computed from FBM by differencing).
about 1 month ago