Brownian bridge

A Brownian bridge is a continuous-time gaussian process B(t) whose probability distribution is the conditional probability distribution of a standard Wiener process W(t) (a mathematical model of Brownian motion) subject to the condition (when standardized) that W(T) = 0, so that the process is pinned to the same value at both t = 0 and t = T. More precisely:

B_{t}:=(W_{t}\mid W_{T}=0),\;t\in [0,T]

The expected value of the bridge at any t in the interval [0,T] is zero, with variance $\textstyle {\frac {t(T-t)}{T}}$ , implying that the most uncertainty is in the middle of the bridge, with zero uncertainty at the nodes. The covariance of B(s) and B(t) is $\min(s,t)-{\frac {s\,t}{T}}$ , or s(T − t)/T if s < t. The increments in a Brownian bridge are not independent.

Relation to other stochastic processes

If ${\textstyle W(t)}$ is a standard Wiener process (i.e., for ${\textstyle t\geq 0}$ , ${\textstyle W(t)}$ is normally distributed with expected value ${\textstyle 0}$ and variance ${\textstyle t}$ , and the increments are stationary and independent), then

B(t)=W(t)-{\frac {t}{T}}W(T)\,

is a Brownian bridge for ${\textstyle t\in [0,T]}$ . It is independent of ${\textstyle W(T)}$ ^[1]

Conversely, if ${\textstyle B(t)}$ is a Brownian bridge for ${\textstyle t\in [0,1]}$ and ${\textstyle Z}$ is a standard normal random variable independent of ${\textstyle B}$ , then the process

W(t)=B(t)+tZ\,

is a Wiener process for ${\textstyle t\in [0,1]}$ . More generally, a Wiener process ${\textstyle W(t)}$ for ${\textstyle t\in [0,T]}$ can be decomposed into

W(t)={\sqrt {T}}B\left({\frac {t}{T}}\right)+{\frac {t}{\sqrt {T}}}Z.

Another representation of the Brownian bridge based on the Brownian motion is, for ${\textstyle t\in [0,T]}$

B(t)={\frac {T-t}{\sqrt {T}}}W\left({\frac {t}{T-t}}\right).

Conversely, for ${\textstyle t\in [0,\infty ]}$

W(t)={\frac {T+t}{T}}B\left({\frac {Tt}{T+t}}\right).

The Brownian bridge may also be represented as a Fourier series with stochastic coefficients, as

B_{t}=\sum _{k=1}^{\infty }Z_{k}{\frac {{\sqrt {2T}}\sin(k\pi t/T)}{k\pi }}

where $Z_{1},Z_{2},\ldots$ are independent identically distributed standard normal random variables (see the Karhunen–Loève theorem).

A Brownian bridge is the result of Donsker's theorem in the area of empirical processes. It is also used in the Kolmogorov–Smirnov test in the area of statistical inference.

Let $K=\sup _{t\in [0,1]}|B(t)|$ , then the cumulative distribution function of ${\textstyle K}$ is given by^[2] $\operatorname {Pr} (K\leq x)=1-2\sum _{k=1}^{\infty }(-1)^{k-1}e^{-2k^{2}x^{2}}={\frac {\sqrt {2\pi }}{x}}\sum _{k=1}^{\infty }e^{-(2k-1)^{2}\pi ^{2}/(8x^{2})}.$

Intuitive remarks

A standard Wiener process satisfies W(0) = 0 and is therefore "tied down" to the origin, but other points are not restricted. In a Brownian bridge process on the other hand, not only is B(0) = 0 but we also require that B(T) = 0, that is the process is "tied down" at t = T as well. Just as a literal bridge is supported by pylons at both ends, a Brownian Bridge is required to satisfy conditions at both ends of the interval [0,T]. (In a slight generalization, one sometimes requires B(t₁) = a and B(t₂) = b where t₁, t₂, a and b are known constants.)

Suppose we have generated a number of points W(0), W(1), W(2), W(3), etc. of a Wiener process path by computer simulation. It is now desired to fill in additional points in the interval [0,T], that is to interpolate between the already generated points W(0) and W(T). The solution is to use a Brownian bridge that is required to go through the values W(0) and W(T).

General case

For the general case when W(t₁) = a and W(t₂) = b, the distribution of B at time t ∈ (t₁, t₂) is normal, with mean

a+{\frac {t-t_{1}}{t_{2}-t_{1}}}(b-a)

and variance

{\frac {(t_{2}-t)(t-t_{1})}{t_{2}-t_{1}}},

and the covariance between B(s) and B(t), with s < t is

{\frac {(t_{2}-t)(s-t_{1})}{t_{2}-t_{1}}}.

References

^ Aspects of Brownian motion, Springer, 2008, R. Mansuy, M. Yor page 2
^ Marsaglia G, Tsang WW, Wang J (2003). "Evaluating Kolmogorov's Distribution". Journal of Statistical Software. 8 (18): 1–4. doi:10.18637/jss.v008.i18.

Glasserman, Paul (2004). Monte Carlo Methods in Financial Engineering. New York: Springer-Verlag. ISBN 0-387-00451-3.
Revuz, Daniel; Yor, Marc (1999). Continuous Martingales and Brownian Motion (2nd ed.). New York: Springer-Verlag. ISBN 3-540-57622-3.

[1] Aspects of Brownian motion, Springer, 2008, R. Mansuy, M. Yor page 2

[2] Marsaglia G, Tsang WW, Wang J (2003). "Evaluating Kolmogorov's Distribution". Journal of Statistical Software. 8 (18): 1–4. doi:10.18637/jss.v008.i18.

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