Brownian bridge

A standard Brownian bridge is the centered Gaussian process $B(t)=W(t)-tW(1)$ on $[0,1]$. Its covariance is $\min (s,t)-st$ and it satisfies $B(0)=B(1)=0$.

The Brownian transition semigroup identity proves that this positive density process has mean one for $t<1$. The backward heat equation and Itô's formula give $d{M}_t=M_t(y-B_t)(1-t{)}^{-1}d{B}_t$. On every fixed horizon $T<1$, Girsanov theorem under $dQ=M_{T}dP$ makes $B_t-{\int }_0^t(y-B_s)(1-s{)}^{-1}ds$ Brownian motion. Thus the original coordinate acquires the bridge drift coefficient toward $y$ at time one, without defining an equivalent endpoint density at the singular time.

The Brownian bridge covariance kernel is invariant under reversing both times, and its mean is zero. The two centered Gaussian processes therefore have equal finite-dimensional laws. Equality on countably many rational coordinates transfers the event of continuity at zero to the reversed endpoint. Interior continuity then extends the rational-time limit to all times. This proves the terminal continuity of the stochastic integral representation of a Brownian bridge.

For $t<1$ this stochastic integral solves $d{X}_t=d{B}_t-X_t(1-t{)}^{-1}dt$. The Itô product rule also gives $X_t=B_t-(1-t){\int }_0^t{B}_s(1-s{)}^{-2}ds$. The process is centered Gaussian, with covariance $\min (s,t)-st$, and has a continuous extension $X_1=0$. Time reversal of a Brownian bridge proves endpoint continuity without assuming it at the start.

For a cumulative distribution function $F$ and a standard Brownian bridge $\mathbb{G}$, the F-Brownian bridge is the centred Gaussian process ${\mathbb{G}}_F=\mathbb{G}\circ F$. Its covariance is $F(\min (s,t))-F(s)F(t)$, by substituting $F(s),F(t)$ into the bridge covariance. It can jump when $F$ jumps. Its law is tight in the supremum norm, since composition with $F$ is a continuous map from the separable space of continuous bridge paths.

For standard Brownian motion, the Brownian bridge $U_t=B_t-t{B}_1$, $0\le t\le 1$, is independent of $B_1$. Every finite vector of bridge coordinates is jointly multivariate normal with $B_1$, and its cross-covariances with $B_1$ vanish. The independence of uncorrelated jointly normal variables and independence extended from generating pi-systems prove independence of the whole stochastic process.

For a one-dimensional diffusion of variance rate $2D$ conditioned to start at $x>0$ and end at $y>0$ after time $\Delta t$, the bridge crosses zero with probability $\exp [-xy/(D\Delta t)]$. This corrects endpoint-only absorption tests in time-stepping schemes.