Past exam of the mathematics course of the University of Cambridge / 2017 / iii / Paper 201 / 1

Relative to a probability space $(\Omega ,\mathcal{F},\mathbb{P})$ and a filtration $({\mathcal{F}}_n)$, the real stochastic process $(M_n)$ is a martingale if it is adapted, each $M_n$ is an integrable random variable, andHere adapted means $M_n$ is ${\mathcal{F}}_n$-measurable, and integrability means $\mathbb{E}|M_n|<\infty$. By the tower property of conditional expectation, the equivalent multi-step condition is $\mathbb{E}[M_j\mid {\mathcal{F}}_i]=M_i$ for all $i\le j$. The filtration is part of the definition; specifying only the marginal means is insufficient.

The discrete-time Doob L2 maximal inequality states that a square-integrable martingale satisfiesEquivalently, the Lp norm of the maximum at $p=2$ is at most $2\Vert M_n{\Vert }_2$. The same assertion holds for a nonnegative square-integrable submartingale. No assumption $M_0=0$ is required; for a bound in terms of fluctuations from the initial value, apply it to $(M_k-M_0)$.

Use the natural filtration ${\mathcal{F}}_k=\sigma (X_1,\dots ,X_k)$ and define $M_k=S_k-mk$. The increments $X_k-m$ are centered, independent, and square integrable, so their conditional expectations given the past are zero. Thus $(M_k)$ is a square-integrable martingale with $M_0=0$. Independence givesApplying the Doob L2 maximal inequality yieldsThis includes ${\sigma }^2=0$, when every centered increment vanishes almost surely.

Put $M_k=S_k-mk$ as above. Centering the linear interpolation also interpolates the centered values: for $u\in [0,1]$,The absolute value of this convex combination is at most the larger endpoint absolute value. For every fixed $A>0$, part (c) therefore givesHence there is uniform convergence on compacts in probability to the deterministic path $h(t)=mt$, by Markov inequality. Equip $C([0,\infty ),\mathbb{R})$ with its standard compact-open topology, metrized byFor each finite number of terms their suprema converge to zero in probability, and the remaining tail is bounded deterministically by ${\sum }_{j>J}{2}^{-j}$. Thus $d(S^{(N)},h)\to 0$ in probability. Convergence in probability to a deterministic point implies weak convergence of probability measures, so the fluid limit isHere ${\delta }_h$ is the Dirac measure concentrated on that continuous path. The topology is locally uniform convergence; no assertion of uniform convergence over the entire unbounded half-line is needed. The interpolation is intended for integers $k\ge 0$, including the initial interval $[0,1]$. If the PDF's ${\mathbb{Z}}^{+}$ is interpreted as strictly positive integers, that initial piece is omitted from the displayed definition and must be supplied by the same formula using $S_0=0$.