Past exam of the mathematics course of the University of Cambridge / 2002 / iii / Paper 31 (Stochastic Calculus and Applications)

Work with the filtration relative to which the given Brownian motion or Markov jump process has its stated properties. All martingale assertions below concern every finite time interval; no infinite-horizon uniform integrability is claimed.

First put $C={\sup }_x|\sigma (x)|$. The solution has the representation $X_t={\int }_0^t\sigma (X_s)d{B}_s$, and the Itô isometry givesThus $X$ is a square-integrable martingale, not just a local martingale. Applying the Itô formula to its square cancels the finite-variation term and givesThe expected squared integrand integral is bounded byTherefore both $X$ and $Z$ are true square-integrable martingales. This is the bounded diffusion coefficient gives square martingales criterion. Only bounded measurability of the coefficient and existence of the stipulated solution have been used.

For the jump process, the Lévy kernel of a Markov jump process uses displacements: a mark $y$ sends the state $x$ to $x+y$. Let $\Lambda ={\sup }_x\lambda (x)$ and $V={\sup }_{x}v(x)$. The bounded total rate prevents explosion; the number of jumps on a finite interval is dominated by a rate-$\Lambda$ Poisson process. Let $N(ds,dy)$ record the jump displacements. Its predictable compensator of a jump measure isThe Cauchy-Schwarz inequality gives $\int |y|K(x,dy)\le \sqrt{\lambda (x)v(x)}$, so the zero first moment is absolutely meaningful. Since that moment vanishes,The compensated integral is a square-integrable martingale: its isometry follows first for bounded marks from conditional compensation and then by truncation in the squared-integrand norm.

The jump version of the square Itô formula is $X_t^2=2{\int }_0^t{X}_{s-}d{X}_s+{\sum }_{s\le t}(\Delta X_s{)}^2$. HenceThe first term is square integrable becauseThe second is an integrable martingale: the expected absolute uncompensated jump-square sum is at most $Vt$, and the compensator identity, multiplied by any bounded event known at an earlier time, makes its increments conditionally centered. Values $X_s$ and $X_{s-}$ agree for Lebesgue-almost every time, so the displayed compensation is exactly the one defining $Z$. Thus $X$ and $Z$ are true martingales in the jump case as well. A fourth moment of the jumps is unnecessary; in particular we have not asserted that this $Z$ must be square integrable. This is centered finite-rate jump kernel gives square martingales.

For $g(x)=\log |x|$ on ${\mathbb{R}}^2\setminus \{0\}$,The Itô formula, stopped in a compact annulus, therefore givesThis proves the local martingale property up to a possible hitting of the origin. For the annulus exit time $T$, $M_{t\wedge T}$ is bounded between $\log r$ and $\log R$, so it is a true uniformly integrable martingale.

The exit is finite almost surely. Indeed $|B_t{|}^2-2t$ is a martingale, so stopping at $T\wedge t$ gives $2\mathbb{E}(T\wedge t)=\mathbb{E}|B_{T\wedge t}{|}^2-1\le R^2-1$. Letting $t\to \infty$ proves $\mathbb{E}T<\infty$. Bounded convergence in the logarithmic martingale now yieldsIf $p$ is the probability of reaching radius $r$ before radius $R$, continuity givesHitting zero before reaching radius $R$ would require reaching every positive radius $r$ first. Its probability is bounded by $p$, which tends to zero as $r\downarrow 0$. Any finite-time path hitting zero is bounded before that time and hence hits zero before exiting some integer-radius disk. A countable union over those disks proves $B_t\ne 0$ for every $t\ge 0$, almost surely. This is planar Brownian motion avoids a fixed point; it also makes $M$ a globally defined continuous local martingale.

To decide whether it is a true martingale, compute its expectation directly. Condition on $B_0$ if necessary and use rotation to take $B_0=(1,0)$ and write $B_t=B_0+W_t$. Conditional on $\rho =|W_t|$, the angle of $W_t$ is uniform. The angular average of a logarithmic potential isFor $\rho <1$ this follows by taking the real part of the convergent power series for $\log (1+\rho e^{i\theta })$: each nonconstant Fourier term integrates to zero. For $\rho >1$ factor out $\rho$ and apply the same argument to $1/\rho$. The equality at one follows by the integrable limiting logarithmic singularity. All interchanges are valid: the shifted Gaussian density is bounded near the origin, where ${\int }_0^{1}r|\log r|dr<\infty$, and its Gaussian tail controls logarithmic growth.

The radius $\rho$ has density $(\rho /t)e^{-{\rho }^2/(2t)}$. Consequently, for $t>0$,Since $M_0=0$, $M$ is not a martingale, despite integrability at each deterministic time. This is the logarithmic radius of planar Brownian motion example.

The optional hint is consistent with this conclusion. The upper-radius exit $T(2,0)$ is finite and never occurs at zero. After that exit the stopped square is $(\log 2{)}^2$; before it, a radius in $[1/2,2]$ also gives a logarithmic square no larger than $(\log 2{)}^2$, while a radius below $1/2$ contributes precisely the extra indicated term. This proves the hinted inequality without assuming that the unstopped process is a true martingale.

A simple predictable process can be writtenwhere each ${\xi }_j$ is bounded and ${\mathcal{F}}_{t_j}$-measurable. Define its integral to start at zero:Refining the partition leaves this sum unchanged. Every summand is a martingale, by conditioning its future increment on the information at $t_j$. Bounded coefficients and the $L^2$ bound on $M$ make the integral an L2-bounded continuous martingale. Its terminal value is the same sum with $M_{\infty }$ substituted at any infinite endpoint; $L^2$ martingale convergence supplies this limit.

We need the conditional square identity, rather than assuming independent increments. The Doob L2 maximal inequality gives $\mathbb{E}{\sup }_t|M_t{|}^2<\infty$. Localize the square identity for $M^2-[M]$ at level and bracket stopping times. The stopped identity and this maximal bound give $\mathbb{E}[M{]}_{\infty }<\infty$ by monotone convergence. Nowis integrable, so localization passes to conditional expectations and $M^2-[M]$ is a uniformly integrable martingale. Expanding a squared increment and using $\mathbb{E}(M_t\mid {\mathcal{F}}_s)=M_s$ provesThe infinite endpoint follows from $L^2$ convergence and integrability of the bracket. The initial value $M_0$ need not be zero: the integral itself is zero-starting.

In the square of the terminal integral, all cross terms vanish. For $j<k$, the $j$th increment and both coefficients in its cross term are measurable at $t_k$, whereas the conditional mean of the $k$th increment there is zero. For the diagonal terms, the preceding conditional identity can be multiplied by the bounded ${\xi }_j^2$. ThereforeThis proves the Itô isometry for these simple integrands. Ordered stopping-time intervals also work, replacing the conditional identity by optional stopping theorem, but deterministic intervals suffice for the approximation requested here.

For that approximation, put $\mu (A)=\mathbb{E}{\int }_0^1{1}_A(\omega ,s)ds$ on the predictable sigma-algebra. This is a finite measure. That sigma-algebra is generated, apart from the zero-time slice of measure zero, by rectangles $A\times (s,t]$ with $A\in {\mathcal{F}}_s$. The indicator of every such rectangle is a bounded simple predictable process.

Here is a density proof. Let $V$ be the closed linear span of these indicators in $L^2(\mu )$, and let $\mathcal{D}$ contain the predictable sets whose indicators belong to $V$. The whole space belongs to $\mathcal{D}$; complements stay in $\mathcal{D}$; and disjoint countable unions do also, because their finite partial indicator sums converge in $L^2$ under the finite measure. Thus $\mathcal{D}$ is a Dynkin system containing the generating rectangle pi-system. The Pi-lambda theorem makes it the entire predictable sigma-algebra. Bounded predictable simple functions therefore belong to $V$. Truncating any $L^2(\mu )$ function and then approximating its truncated range by finitely many levels shows that every such function belongs to $V$. A finite sum of rectangle indicators can be rewritten on one common deterministic time partition, with the coefficient on each interval measurable at its left endpoint; it is exactly an allowed simple process.

Consequently choose $H^n$ of this form with $\Vert H^n-H{\Vert }_{L^2(\mu )}<1/n$. This provesIt is density of simple predictable processes for finite measures, with a proof of the actual approximation step.

Finally apply the preceding isometry to $M_t=B_{t\wedge 1}$, whose bracket is $t\wedge 1$. The elementary integrals $I_n={\int }_0^1{H}_s^{n}d{B}_s$ satisfyDefine the Itô integral as the $L^2$ limit of these elementary integrals. The same identity shows independence of the approximating sequence, linearity, mean zero, and $\mathbb{E}({\int }_0^1{H}_{s}d{B}_s{)}^2=\mathbb{E}{\int }_0^1{H}_s^{2}ds$. Applying the Doob inequality to the difference processes also gives uniform-in-time $L^2$ convergence on $[0,1]$, with a continuous martingale version of the integral.

For $\theta >0$, $E_s=\exp (\theta B_s-{\theta }^{2}s/2)$ is an Exponential martingale for Brownian motion, as follows directly from independent Gaussian increments. Stop it at ${\tau }_{\delta }\wedge t$, where ${\tau }_{\delta }$ is the first time $B$ reaches $\delta$. On $\{{\tau }_{\delta }\le t\}$,The stopped expectation is one and the other contribution is nonnegative, soFor $t>0$, minimize the exponent at $\theta =\delta /t$. Apply the same argument to $-B$ and use the union bound to obtainAt $t=0$ the probability is zero; the right side can be interpreted by its limit. This proves the Gaussian maximal bound for Brownian motion without losing an extra factor from a two-sided reflection argument.

Let $L$ be a global Lipschitz constant of $b$. Realize the diffusion with the stated generator by $d{X}_s^{\epsilon }=b(X_s^{\epsilon })ds+\epsilon d{W}_s$, where $W$ is a standard $d$-dimensional Brownian motion. Global Lipschitz continuity gives existence and uniqueness; its generator is exactly the printed operator. Couple it with the ordinary differential equation having the same initial value $x(0)=x_0$. Subtraction gives, with $D_t={\sup }_{s\le t}|X_s^{\epsilon }-x_s|$,The Gronwall inequality implies $D_t\le \epsilon e^{Lt}{\sup }_{s\le t}|W_s|$. If a Euclidean norm exceeds $u$, some coordinate has absolute value exceeding $u/\sqrt{d}$. The scalar bound and a union over the coordinates therefore giveTaking the logarithm and multiplying by ${\epsilon }^2$ yields the explicit answerFor $t=0$ the probability is zero and the limit is $-\infty$, with $\log 0=-\infty$. This is exponential small-noise concentration for a Lipschitz diffusion.

Matching the deterministic initial value is necessary. The printed last line specifies the differential equation but does not separately write this initial condition. Without it, take $b=0$ and a deterministic solution displaced from $x_0$ by $2\delta$ in one fixed direction. The deviation event already holds at time zero, giving logarithmic rate zero instead of a strictly negative rate.

Write $f=u+iv$. The Cauchy-Riemann equations give $v_x=-u_y$, $v_y=u_x$ and $\Delta u=\Delta v=0$. Since the two Brownian coordinates are independent, $[X{]}_t=[Y{]}_t=t$ and $[X,Y{]}_t=0$. The Itô formula therefore becomesStop in balls on which the derivatives are bounded. The stopped integrals are martingales, proving that $U$ and $V$ are local martingales. Their coefficient rows have equal squared length and are orthogonal. ThusThese are quadratic covariations of an analytic Brownian image; orthogonality does not in general imply independence of the two transformed coordinates.

For the exit law, put $c=2a^2$. Squaring sends the positive quadrant to the upper half-plane, sends $a+ia$ to $ic$, and the Möbius transformation $w\mapsto (w-ic)/(w+ic)$ sends the upper half-plane to the unit disk with the starting point sent to zero. Its composition is the displayed conformal map. The exit time is finite: it is the minimum of the two almost surely finite one-dimensional hitting times of zero. There is no hit at the quadrant's corner, since a planar Brownian motion started away from zero avoids that point.

We can justify the uniform image exit law without assuming a conformal-time-change theorem. For each integer $n\ge 1$, the real and imaginary parts of $f(Z_{t\wedge T}{)}^n$ are bounded martingales obtained from harmonic functions. At the start their values are zero. The bounded convergence theorem at the finite exit time gives $\mathbb{E}f(Z_T{)}^n=0$. The boundary image has modulus one, and conjugation gives the vanishing negative Fourier moments as well. Trigonometric polynomials are dense among continuous functions on the unit circle, so these moments characterize the uniform distribution there.

Consequently the real variable $W=Z_T^2$ is the inverse image of a uniform point $e^{i\Theta }$ under that Möbius transformation:The Jacobian of this change of variable gives the centered Cauchy distribution of scale $c$:Positive $w$ corresponds to exit at $\sqrt{w}$ on the real axis; negative $w$ corresponds to exit at $i\sqrt{-w}$ on the imaginary axis. Pulling back by $w=\pm r^2$ therefore gives the complete boundary lawHere $dr$ on each axis denotes length along that axis, not two-dimensional area measure. Each density integrates to $1/2$, with no remaining atoms. Equivalently the chosen axis is a fair Bernoulli choice independent of the radius, andThis is Brownian exit law from a quadrant, obtained as a harmonic measure and not as an absolutely continuous planar density.

Here is a finite-horizon Girsanov theorem statement with the sign fixed explicitly. Let $B$ be a Brownian motion under $P$, and let $\theta$ be predictable with ${\int }_0^T{\theta }_s^{2}ds<\infty$ almost surely. Ifis a true martingale with $\mathbb{E}{L}_T=1$, then the measure $Q$ defined by $dQ=L_{T}dP$ makes $B_t-{\int }_0^t{\theta }_{s}ds$ a Brownian motion up to $T$. The sufficient Novikov condition is $\mathbb{E}\exp (\frac{1}{2}{\int }_0^T{\theta }_s^{2}ds)<\infty$. Merely being a local martingale is not a sufficient density condition.

For a deterministic absolutely continuous function $h$ with $h(0)=0$ and derivative $g\in L^2[0,T]$, take $\theta =g$. Novikov's condition holds because its exponential is deterministic and finite. Under $Q$, the canonical process has the law of a Brownian motion plus $h$. Thus for every bounded measurable path functional $F$,This derives the Cameron-Martin theorem density formula from the Girsanov theorem, rather than assuming the two measures have the same drift convention. Replacing $h$ by $-h$ changes the sign of the stochastic integral.

The question's path space is the whole half-line, not just one finite horizon. When $g\in L^2(0,\infty )$, the density martingale satisfiesIt has a uniformly integrable limit $L_{\infty }=\exp ({\int }_0^{\infty }{g}_{s}d{B}_s-\Vert g{\Vert }_2^2/2)$ of mean one, strictly positive almost surely. Integrals here converge in $L^2$ and almost surely. The finite-time identities then identify $L_{\infty }$ as the density on the full path sigma-algebra, since finite-time cylinder events generate it. This is Cameron-Martin shifts on infinite Wiener path space. Finite-horizon absolute continuity of measures alone does not imply infinite-horizon absolute continuity of measures.

There is no density on the full infinite path space; the two laws are mutually singular. Under Wiener measure, $B_t/t\to 0$ almost surely. Under the shifted law, $(B_t+t)/t\to 1$ almost surely, so the same measurable tail event separates the laws.

For completeness the Brownian limit follows from the Gaussian bound at integers and the maximal bound on each interval $[n,n+1]$: for every fixed $\eta >0$, both ${\sum }_n\mathbb{P}(|B_n|>\eta n)$ and ${\sum }_n\mathbb{P}({\sup }_{0\le s\le 1}|B_{n+s}-B_n|>\eta n)$ are finite. The Borel-Cantelli lemma and continuity fill the gaps between integers. Intersect over positive rational $\eta$. This proves the limit and the claimed singularity, which is infinite-horizon singularity of Brownian motion with constant drift.

On every finite horizon there nevertheless is the density $e^{B_T-T/2}$. Its failure to give a global density is exactly the distinction required by the printed half-line path space.

The deterministic shift has derivative $g_s=-1_{[0,1]}(s)$ and finite total energy one. The infinite-horizon Cameron-Martin formula therefore givesThe integral of this density is one by the moment-generating function of $B_1\sim N(0,1)$. It is strictly positive, so the shifted law and Wiener measure are equivalent on the whole half-line. The density depends only on the path through time one; after that time the shift is constant.

There is no density; scaling by two gives a law singular to Wiener measure. On $[0,1]$ form the pathwise dyadic sumsUnder Wiener measure, independent normal increments give $\mathbb{E}{Q}_n=1$ and $\mathrm{var}(Q_n)=2^{1-n}$. Chebyshev's inequality and the Borel-Cantelli lemma show $Q_n\to 1$ almost surely, because the variances are summable. For $2B$, every sum is four times its Brownian counterpart and hence tends to four almost surely. The event that the limit equals one separates the two laws. Unlike the linear-drift case, this obstruction already occurs on a finite interval: it is the Pathwise quadratic variation distinguishes Brownian speeds argument.

The PDF's subscript is $0$, and the Brownian motion starts from zero. Thus $B_0=0$ almost surely and the process is exactly $B$ at every time. Its law is Wiener measure itself, with density $1$. There is no random future coefficient or anticipating transformation in this printed case.