# Fractional Brownian motion

In probability theory, **fractional Brownian motion (fBm)**, also called a **fractal Brownian motion**, is a generalization of Brownian motion. Unlike classical Brownian motion, the increments of fBm need not be independent. fBm is a continuous-time Gaussian process *B _{H}*(

*t*) on [0,

*T*], which starts at zero, has expectation zero for all

*t*in [0,

*T*], and has the following covariance function:

where *H* is a real number in (0, 1), called the Hurst index or Hurst parameter associated with the fractional Brownian motion. The Hurst exponent describes the raggedness of the resultant motion, with a higher value leading to a smoother motion. It was introduced by Template:Harvtxt.

The value of *H* determines what kind of process the *fBm* is:

- if
*H*= 1/2 then the process is in fact a Brownian motion or Wiener process; - if
*H*> 1/2 then the increments of the process are positively correlated; - if
*H*< 1/2 then the increments of the process are negatively correlated.

The increment process, *X*(*t*) = *B _{H}*(

*t*+1) −

*B*(

_{H}*t*), is known as

**fractional Gaussian noise**.

There is also a generalization of fractional Brownian motion: ** n-th order fractional Brownian motion**, abbreviated as n-fBm.

^{[1]}n-fBm is a Gaussian, self-similar, non-stationary process whose increments of order

*n*are stationary. For

*n*= 1, n-fBm is classical fBm.

Like the Brownian motion that it generalizes, fractional Brownian motion is named after 19th century biologist Robert Brown; fractional Gaussian noise is named after mathematician Carl Friedrich Gauss.

## Background and definition

Prior to the introduction of the fractional Brownian motion, Template:Harvtxt used the Riemann–Liouville fractional integral to define the process

where integration is with respect to the white noise measure *dB*(*s*). This integral turns out to be ill-suited to applications of fractional Brownian motion because of its over-emphasis of the origin Template:Harv.

The idea instead is to use a different fractional integral of white noise to define the process: the Weyl integral

for *t* > 0 (and similarly for *t* < 0).

The main difference between fractional Brownian motion and regular Brownian motion is that while the increments in Brownian Motion are independent, the opposite is true for fractional Brownian motion. This dependence means that if there is an increasing pattern in the previous steps, then it is likely that the current step will be increasing as well. (If H > 1/2.)

## Properties

### Self-similarity

The process is self-similar, since in terms of probability distributions:

This property is due to the fact that the covariance function is homogeneous of order 2H and can be considered as a fractal property. Fractional Brownian motion is the only self-similar Gaussian process.

### Stationary increments

It has stationary increments:

### Long-range dependence

For *H* > ½ the process exhibits long-range dependence,

### Regularity

Sample-paths are almost nowhere differentiable. However, almost-all trajectories are Hölder continuous of any order strictly less than *H*: for each such trajectory, there exists a constant *c* such that

for every *ε* > 0.

### Dimension

With probability 1, the graph of *B _{H}*(

*t*) has both Hausdorff dimension and box dimension of 2−

*H*.Template:Cn

### Integration

As for regular Brownian motion, one can define stochastic integrals with respect to fractional Brownian motion, usually called "fractional stochastic integrals". In general though, unlike integrals with respect to regular Brownian motion, fractional stochastic integrals are not semimartingales.

## Sample paths

Practical computer realisations of an *fBm* can be generated, although they are only a finite approximation. The sample paths chosen can be thought of as showing discrete sampled points on an *fBm* process. Three realisations are shown below, each with 1000 points of an *fBm* with Hurst parameter 0.75.

Two realisations are shown below, each showing 1000 points of an *fBm*, the first with Hurst parameter 0.95 and the second with Hurst parameter 0.55.

### Method 1 of simulation

One can simulate sample-paths of an *fBm* using methods for generating stationary Gaussian processes with known covariance function. The simplest method
relies on the Cholesky decomposition method of the covariance matrix (explained below), which on a grid of size
has complexity of order . A more complex, but computationally faster method is the circulant embedding method of Template:Harvtxt.

Suppose we want to simulate the values of the *fBM* at times using the Cholesky decomposition method.

- Compute the square root matrix of , i.e. . Loosely speaking, is the "standard deviation" matrix associated to the variance-covariance matrix .

In order to compute , we can use for instance the Cholesky decomposition method. An alternative method uses the eigenvalues of :

- Since is symmetric, positive-definite matrix, it follows that all eigenvalues of satisfy , ().

- Let be the diagonal matrix of the eigenvalues, i.e. where is the Kronecker delta. We define as the diagonal matrix with entries , i.e. .

Note that the result is real-valued because .

- Let an eigenvector associated to the eigenvalue . Define as the matrix whose -th column is the eigenvector .

Note that since the eigenvectors are linearly independent, the matrix is inversible.

### Method 2 of simulation

It is also known that{{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
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where *B* is a standard Brownian motion and

Where is the Euler hypergeometric integral.

Say we want simulate an *fBm* at points .

- Construct a vector of
*n*numbers drawn according a standard Gaussian distribution.

- Multiply it component-wise by √(
*T*/*n*) to obtain the increments of a Brownian motion on [0,*T*]. Denote this vector by .

The integral may be efficiently computed by Gaussian quadrature. Hypergeometric functions are part of the GNU scientific library.

## See also

- Brownian surface
- Autoregressive fractionally integrated moving average
- Multifractal: The generalized framework of fractional Brownian motions.
- Pink noise
- Tweedie distributions

## Notes

- ↑ Perrin et al., 2001.

## References

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|CitationClass=citation }}.

- Craigmile P.F. (2003), "Simulating a class of stationary Gaussian processes using the Davies–Harte Algorithm, with application to long memory processes",
*Journal of Times Series Analysis*, 24: 505–511. - Template:Cite thesis
- {{#invoke:citation/CS1|citation

|CitationClass=citation }}.

- {{#invoke:citation/CS1|citation

|CitationClass=citation }}.

- {{#invoke:citation/CS1|citation

|CitationClass=citation }}.

- Perrin E. et al. (2001), "nth-order fractional Brownian motion and fractional Gaussian noises",
*IEEE Transactions on Signal Processing*, 49: 1049-1059. Template:Hide in printTemplate:Only in print - Samorodnitsky G., Taqqu M.S. (1994),
*Stable Non-Gaussian Random Processes*, Chapter 7: "Self-similar processes" (Chapman & Hall).