Denote the density process by . The given identities for all implyThus is a strictly positive true martingale, not merely a local martingale. The Itô formula gives , and henceFirst work with the zero-starting corrected increments . Integration by parts givesThe finite-variation bracket has zero quadratic covariation with , so no other term appears. Thus is a P-local martingale.
To justify the change of measure, stop at , with optional further localization if needed. The product remains a P-local martingale: before the stopping time the cancellation above applies, and afterwards only its bounded constant factor multiplies . Its absolute value is bounded by . On a finite horizon the stopped values of are uniformly integrable, so Q1(c) upgrades this product to a true martingale. The Bayes formula for conditional expectation now givesFinite-horizon equivalence of the measures transfers the localizing sequence to Q. Therefore is a continuous Q-local martingale. If , adding its constant-in-time process provesThis proves the Girsanov theorem assertion under the usual zero or deterministic initial-value convention. More generally the normalization makes , so P and Q agree on and the required initial integrability is automatic.
There is a literal initial-integrability qualification if neither convention is imposed. On positive integers let , , with , and let every filtration equal the full sigma-algebra. Constant processes and are P-martingales, and is precisely the prescribed density. But and , so the asserted Q-local martingale cannot have an integrable initial value. This is why initial integrability under a density change is part of the precise formulation.
If is a P-Brownian motion, its starting value is deterministic, so Q5(a) applies. The correction has finite variation and does not alter quadratic variation; Q1(b) gives under Q. The Lévy characterization of Brownian motion therefore identifies as a Q-Brownian motion with the same starting value.
For the likelihood, use Wiener measure as the reference measure and write its coordinate as . Setand let be its stochastic exponential. We must prove it is a true martingale; unverified large-horizon Novikov estimates would not suffice.
Fix and stop at , with . The bounded integrand satisfies the Novikov condition, so defines a probability on . By the result just established,is Brownian under on this horizon. Therefore . The Gronwall inequality givesThe Doob L2 maximal inequality then bounds its squared expectation by , uniformly in . In particular . On the stopped and unstopped densities agree, soContinuous Brownian paths are bounded on , and the indicators increase to one. The monotone convergence theorem proves . Since a nonnegative local martingale is a supermartingale, constant expectation at every time makes a true martingale.
Under its tilted measure, is Brownian and . The explicit linear solution gives uniqueness of this law, identifying it with . Consequently the Ornstein-Uhlenbeck likelihood relative to Wiener measure isThis is a finite-horizon density statement; it does not assert absolute continuity of the full infinite-horizon path laws.
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