For started at a point of radius one, its radial process has the law of the inverse-clock process , where inverts . The logarithmic growth of an exponential Brownian clock with positive drift gives , whereas . Substituting proves the displayed almost sure escape rate and divergence of the radius. Independently, Brownian scaling gives the same rate in probability; if an almost sure logarithmic rate is already known to exist, that identifies its value.
Put . First verify that the origin is not reached, so that differentiating the radius is legitimate. On the function is harmonic. For with , the Itô formula makes a martingale on each finite horizon: its stochastic integrand has norm at most . Since ,
Let , then . Any path hitting zero must first hit every positive , so .
For , and in three dimensions. Itô's formula, initially localized away from zero, yields
The continuous local martingale has bracket and starts at zero. By the Lévy characterization of Brownian motion, is Brownian motion. Thus
This proves that the radial process is a three-dimensional Bessel process solving the same equation as in part (b). The permitted uniqueness in distribution identifies their laws as processes.
The strong law in part (a) gives , and the inverse clock satisfies . Hence . Equality of process laws transfers this almost sure path property to .
For the further logarithmic rate, fix . Almost surely, for all sufficiently large , . With a finite random lower cutoff , for ,
Taking logarithms and dividing by gives lower and upper asymptotic bounds and . Letting along a countable sequence proves the logarithmic growth of an exponential Brownian clock with positive drift, . Also . Since ,
Again transfer the full path event by uniqueness in distribution. We obtain the logarithmic escape rate of three-dimensional Brownian motion
For an independent way to identify the value once existence of the almost sure limit is granted, use Brownian scaling. At each fixed , has the law of , where is standard three-dimensional Gaussian. Its norm converges in distribution to the strictly positive finite variable , so is tight. Therefore
An existing almost sure limit must agree with this probability limit. This guesses and identifies the value through scaling, while the inverse-clock argument proves that the almost sure limit actually exists.