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Distribution of a one-sided Brownian hitting time

Statement

Assume the Axiom of Choice, let B be a standard Brownian motion Brownian motion. Use the everywhere-continuous, zero-start representative of Law of the Brownian maximum: replace paths by zero outside a measurable probability-one event of continuity and zero start, retaining the notation B. Let a>0 and τa:=inf⁡{t≥0:Bt=a}, with inf⁡∅=+∞. This is a measurable [0,∞]-valued hitting time for this representative; its distribution does not depend on the chosen full-measure event. With Φ the standard normal distribution function Standard normal and normal laws Cumulative distribution function of a real random variable, P(τa≤t)=2(1−Φ ⁣(at))(t>0), and on t>0 the law of τa has the density g(t)=a(2πt3)1/2exp⁡ ⁣(−a22t). Moreover lim⁡t→∞P(τa≤t)=1, so τa is finite almost surely and there is no mass at infinity, and P(τa=0)=0.

Facts & Assumptions

Given: AC, a standard Brownian motion B in this everywhere-continuous zero-start representative, a>0 and t>0.

[F1]

For the representative in the statement, Mt=sup⁡[0,t]B is a finite measurable random variable and P(Mt≤x)=2Φ(x/t)−1 for x≥0 (Law of the Brownian maximum). The closed-set hitting-time lemma applies to an everywhere-continuous Brownian process with its own natural filtration (Brownian closed-set hitting times are stopping times). Brownian motion supplies a measurable full-measure continuity and zero-start event (Brownian motion).

[F2]

Limits of the standard normal distribution function: lim⁡x→+∞Φ(x)=1, Φ(0)=1/2 and Φ is continuous, and Φ(v)−Φ(u)=∫uvφ(x)dx for u<v; φ(u)=e−u2/2/2π. Standard normal and normal laws Cumulative distribution function of a real random variable The standard normal density has total mass one

[F4]

A probability measure on R is determined by its distribution function; countable choice, which AC supplies, is used there. Probability laws correspond to distribution functions The Axiom of Countable Choice (ACω) The Axiom of Choice

Proof

technique · direct
1.1F1given

Fix the measurable full-measure event A specified in the statement. Replacing the original path by zero on Ac preserves every finite-dimensional law and makes every path continuous with B0=0. Each coordinate remains measurable because A is measurable. Two such choices agree on the intersection of their events, so the resulting measurable hitting times agree there and have the same distribution. By [F1] applied to the closed singleton {a}, τa is a stopping time for the chosen process's own raw natural filtration, hence an extended nonnegative measurable random variable. No stopping-time claim for the original raw filtration is used.

1.2F2F3

Define g(s)=a(2πs3)−1/2e−a2/(2s) for s>0. For 0<ε<t, the substitution u=a/s on [ε,t], whose derivative −a/(2s3/2) is continuous and 2φ is continuous, gives ∫εtg(s) ds=∫a/ta/ε2φ(u) du=2(Φ(a/ε)−Φ(a/t)) by oriented substitution, the compact Riemann/Lebesgue bridge, and the density-integral identity for increments of Φ.

2.1F1F5step 1.1

For t>0, if τa≤t, the first hit is attained by continuity (as in the closed-set hitting lemma), so Mt≥a. Conversely Mt≥a gives a time s∈[0,t] with Bs=Mt by [F5]; since B0=0<a≤Bs, the intermediate value theorem gives a hit by time s. Hence {τa≤t}={Mt≥a} as exact measurable events for this representative. Continuity at zero also gives τa>0 on every path, since B0=0<a.

3.1F1F2F3step 2.1

The normal CDF obeys ∣Φ(v)−Φ(u)∣≤∣v−u∣/2π, by its density bound, hence is continuous. Symmetry and total mass one give Φ(0)=1/2; monotone convergence of density integrals gives Φ(x)→1 as x→∞. Put xn=a(1−1/n) for n≥1. The measurable events {Mt≤xn} increase to {Mt<a}, so [F3] and [F1] give P(Mt<a)=lim⁡n(2Φ(xn/t)−1)=2Φ(a/t)−1=P(Mt≤a). Thus P(Mt=a)=0, and step 2.1 yields P(τa≤t)=2(1−Φ(a/t)). Taking integer t→∞ and monotone convergence of the events {τa≤t} gives P(τa<∞)=1.

4.1F2F3step 3.1step 1.2

Let ε=t/n in step 1.2 and let integers n≥2 tend to infinity. The nonnegative integrals increase to ∫0tg(s)ds, and Φ(a/ε)→1, so ∫0tg(s)ds=2(1−Φ(a/t))=P(τa≤t). Letting integer t→∞ now gives ∫0∞g=1.

5.1F4F5step 2.1step 3.1step 4.1

Extend g by zero on (−∞,0]. It is nonnegative Borel measurable, and [F5] and step 4.1 make its density measure a Borel probability measure on R. To use [F4] with a real random variable, replace τa=∞ by the value 1 on its measurable null event, obtaining τ~a. This leaves every finite-time distribution probability unchanged, and τ~a>0 by step 2.1. The density measure and τ~a have CDF zero for nonpositive arguments, and the same CDF at every positive argument by step 4.1. Thus [F4] identifies the laws. In particular the original extended hitting time has density g on (0,∞), no atom there or at zero, and no mass at infinity.

6.1F1F3F4step 2.1step 3.1step 4.1step 5.1∎

The parameter a>0 and compact substitution bounds 0<ε<t ensure every denominator is positive. At t=0, step 2.1 gives P(τa=0)=0. The infinity limit and total density mass were proved in steps 3.1 and 4.1. AC supplies the Countable Choice hypotheses of both the compact integration bridge and [F4], and the Brownian and hitting-time suppliers. The event equality uses the declared continuous representative throughout.

The proof combines the Brownian maximum law with an exact continuous-path hitting identity. It computes the density integral by compact substitution, the Riemann/Lebesgue bridge and monotone limits, then identifies probability laws through their CDFs on all real arguments.

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