Brownian bridges were described in a previous post, along with various different methods by which they can be constructed. Since a Brownian bridge on an interval is continuous and equal to zero at both endpoints, we can consider extending to the entire real line by partitioning the real numbers into intervals of length *T* and replicating the path of the process across each of these. This will result in continuous and periodic sample paths, suggesting another method of representing Brownian bridges. That is, by Fourier expansion. As we will see, the Fourier coefficients turn out to be independent normal random variables, giving a useful alternative method of constructing a Brownian bridge.

There are actually a couple of distinct Fourier expansions that can be used, which depends on precisely how we consider extending the sample paths to the real line. A particularly simple result is given by the sine series, which I describe first. This is shown for an example Brownian bridge sample path in figure 1 above, which plots the sequence of approximations formed by truncating the series after a small number of terms. This tends uniformly to the sample path, although it is quite slow to converge as should be expected when approximating such a rough path by smooth functions. Also plotted, is the series after the first 100 terms, by which time the approximation is quite close to the target. For simplicity, I only consider *standard* Brownian bridges, which are defined on the unit interval . This does not reduce the generality, since bridges on an interval can be expressed as scaled versions of standard Brownian bridges.

Theorem 1A standard Brownian bridgeBcan be decomposed as

(1)

over, whereis an IID sequence of standard normals. This series converges uniformly int, both with probability one and in thenorm for all.

*Proof:* We extend the Brownian bridge to be of period 2 and odd. That is, it is first extended to the interval by setting , and then to the rest of the real number line by periodicity, . The Fourier sine series expansion is,

We will deal with the uniform convergence in a moment but, first, let us compute the Fourier coefficients. We may suppose that the Brownian bridge is constructed from a Brownian motion *X* by for . Then, integration by parts gives

As these are integrals of deterministic functions with respect to Brownian motion, they are joint normal with zero mean and covariances,

For , this integral is zero and, hence, is a sequence of independent normal random variables. Also, taking gives and, hence, is a sequence of independent standard normals. Substituting this into the Fourier series gives (1).

Only uniform convergence remains to be shown. Since we know that Brownian motion sample paths are locally Hölder continuous, I make use of a standard result for convergence of Fourier series of Hölder continuous functions. Using to denote the sum of the first *N* terms on the right hand side of (1), we need to show that tends to zero both almost surely and in the norms as *N* goes to infinity, for each .

If the sample path is -Hölder continuous with coefficient , then the approximations satisfy the bound

for a constant *K* independent of both and of the sample path (Dunham Jackson *The theory of Approximation*, AMS Colloquium Publication Volume XI, New York 1930). In particular, so long as we can find and such that and is -integrable then,

This implies that uniformly both with probability one and in the norm. To find suitable and *p*, we use the Kolmogorov continuity theorem. For any times the covariance structure of a Brownian bridge shows that has variance bounded by and, hence,

for any and constant . The continuity theorem states that the sample paths are -Hölder continuous for all and, furthermore, the -Hölder coefficient is -integrable. So long as , we can ensure that , proving uniform convergence of the sine series both almost surely and in the norm. Finally, as the norm is increasing in *p*, this ensures convergence in the norm for all . ⬜

An alternative Fourier expansion can be used, which has both sine and cosine terms so s a little more complicated. However, it is still the case that the coefficients are independent normals, and this expansion is sometimes preferred to the sine series given above.

Theorem 2A Brownian bridgeBcan be decomposed as,

(2)

over, whereis an IID sequence of standard normals andis the sample mean ofB,

(3)

The series converges uniformly int, both with probability one and in thenorm for all.

*Proof:* We extend the Brownian bridge continuously on the real line to be of period 1, , which uniquely determines *B* in terms of its values on the unit interval . The Fourier series decomposition is,

As above, we suppose that the Brownian bridge is constructed from a standard Brownian motion *X* by . Applying integration by parts,

As they are integrals of a deterministic function with respect to Brownian motion, these coefficients are joint normal with zero mean and correlations given by,

Consequently, whenever . Hence, are independent random variables and, by the expression above, they have variances . Hence, and are independent standard normals. Substituting these coefficients back into the Fourier expansion gives the first line of (2). Evaluating at gives the expression (3) for and, substituting this back into the Fourier expansion gives the second line of (2).

Finally, uniform convergence of the Fourier expansion follows using exactly the same argument as in the proof of theorem 1 above. ⬜

With being as in (2), consider complex-valued random variables . These are IID with real and imaginary parts being independent standard normals. It is straightforward to see that has the same distribution as for any with . Using to denote the fractional part of real number *t*, we can rewrite (2) as

for all . Now, for any fixed , the effect of replacing *t* by on the right hand side is the same as multiplying by which, as we already noted, does not affect its distribution. This gives an alternative way to see that the joint distribution of is translation invariant with respect to the time index.

In case it of use to anyone, the python3 code for generating the plot at the top of the post is below.

As all of the Brownian bridge samples and terms in the sine series are joint normal, I just compute their covariances and use the numpy multivariate_normal function to generate the samples.

First, I would like to thank M.Lowther for the diversity and quality of the probabilistic content we have the happiness to discover on his website. As a young probabilist, it’s always a pleasure to perfect my knowledge with this kind of ressources and I hope this website can also make some newcomers discover the beauty of probability.

I am a bit perplex on the way you derive the Hölder continuity of the Brownian bridge. Precisely this sentence : “For any times {0\le s\le t\le1} the covariance structure of a Brownian bridge shows that {B_t-B_s} has variance bounded by {t-s} and, hence,

“.

I know how to obtain this property thanks to Cameron Martin theorem but how do you get this inequality from the previous one on the order 2 moment? It is probably an inequality like Cauchy Schwarz or Hölder but I don’t see the right one.

Thanks again for your answer.

Hi pascalcule,

This is just a simple consequence of normality. Hölder, Cauchy-Schwarz, etc, are not needed.

As

B_t – B_sis normal with zero mean, it is equal to σX for a standard normal X. So, its p’th moment is σ^pE[X^p]. As was noted, σ^2 is bounded by a multiple of t – s, and E[X^p] is a constant (depending only on p), giving the inequality you quote.