Past exam of the mathematics course of the University of Cambridge / 2023 / iii / Paper 202 / 5

The drift $v/2$ is continuously differentiable and hence locally Lipschitz on the open interval $(0,1)$, while the diffusion coefficient is the constant one. The local existence and pathwise uniqueness theorem for a stochastic differential equation therefore gives a unique strong solution up to its first exit from every compact subinterval. These solutions agree by pathwise uniqueness, producing a unique maximal local solution of a stochastic differential equation whose lifetime is

Since $V^{\prime }=v$, the fundamental theorem of calculus givesApplying the Itô formula before the lifetime, the two drift terms cancel:Thus $\psi (X)$ is a continuous local martingale. The increasing function $\psi$ is the scale function of a one-dimensional diffusion.

Because $v\in L^1(0,1)$, its indefinite integral $V$ is bounded, so $e^{-V}$ is bounded above and away from zero. Hence $\psi$ extends continuously and strictly increasingly to $[0,1]$. The quadratic-variation clock of $\psi (X)$ iswhose rate is bounded above and away from zero before exit. The Dambis-Dubins-Schwarz theorem therefore identifies $\psi (X)$, up to an equivalent time change, with Brownian motion in the bounded interval $(0,\psi (1))$; in particular, $\mathcal{T}<\infty$ almost surely.

The bounded stopped local martingale $\psi (X_{t\wedge \mathcal{T}})$ is a martingale. If $q={\mathbb{P}}_x(X_{\mathcal{T}}=0)$, the optional sampling theorem givesThereforeThis is the boundary hitting probability from a diffusion scale function.

Let ${\mathcal{T}}_n$ be the first exit from $[1/n,1-1/n]$. On that compact interval the drift $v/2$ is bounded. The Girsanov theorem therefore gives, through every fixed time $t$, a probability measure equivalent to the original one under which the stopped process $X_{\cdot \wedge {\mathcal{T}}_n}$ has Brownian increments before ${\mathcal{T}}_n$.

If $A\subset (0,1)$ has Lebesgue measure zero, the normal distribution of Brownian motion givesSince $\{t<\mathcal{T}\}={\bigcup }_n\{t<{\mathcal{T}}_n\}$, countable subadditivity gives $\mathbb{P}(X_t\in A,t<\mathcal{T})=0$. Thus the killed law at time $t$ is absolutely continuous with respect to Lebesgue measure on $(0,1)$.

The killed transition operator isThe infinitesimal generator of the diffusion isThe Markov property and the Chapman-Kolmogorov equation give, for each fixed $y$,Dividing by $h$, letting $h\downarrow 0$, and using the generator definition together with the assumed $C^{1,2}$ regularity gives the pointwise Kolmogorov backward equation