Critical Branching Random Walks with Small Drift

We study critical branching random walks (BRWs) $U^{(n)}$ on~$\mathbb{Z}_{+}$ where for each $n$, the displacement of an offspring from its parent has drift~$2\beta/\sqrt{n}$ towards the origin and reflection at the origin. We prove that for any~$\alpha>1$, conditional on survival to generation~$[n^{\alpha}]$, the maximal displacement is asymptotically equivalent to $(\alpha-1)/(4\beta)\sqrt{n}\log n$. We further show that for a sequence of critical BRWs with such displacement distributions, if the number of initial particles grows like~$yn^{\alpha}$ for some $y>0$ and $\alpha>1$, and the particles are concentrated in~$[0,O(\sqrt{n})],$ then the measure-valued processes associated with the BRWs, under suitable scaling converge to a measure-valued process, which, at any time~$t>0,$ distributes its mass over~$\mathbb{R}_+$ like an exponential distribution

On the Maximal Displacement of Subcritical Branching Random Walks

We study the maximal displacement of a one dimensional subcritical branching random walk initiated by a single particle at the origin. For each $n\in\mathbb{N},$ let $M_{n}$ be the rightmost position reached by the branching random walk up to generation $n$. Under the assumption that the offspring distribution has a finite third moment and the jump distribution has mean zero and a finite probability generating function, we show that there exists $\rho>1$ such that the function g(c,n):=\rho ^{cn} P(M_{n}\geq cn), \quad \mbox{for each }c>0 \mbox{ and } n\in\mathbb{N}, satisfies the following properties: there exist $0<\underline{\delta}\leq \overline{\delta} < {\infty}$ such that if $c<\underline{\delta}$, then $$0<\liminf_{n\rightarrow\infty} g (c,n)\leq \limsup_{n\rightarrow\infty} g (c,n) {\leq 1},$$ while if $c>\overline{\delta}$, then $$\lim_{n\rightarrow\infty} g (c,n)=0.$$ Moreover, if the jump distribution has a finite right range $R$, then $\overline{\delta} < R$. If furthermore the jump distribution is "nearly right-continuous", then there exists $\kappa\in (0,1]$ such that $\lim_{n\rightarrow \infty}g(c,n)=\kappa$ for all $c<\underline{\delta}$. We also show that the tail distribution of $M:=\sup_{n\geq 0}M_{n}$, namely, the rightmost position ever reached by the branching random walk, has a similar exponential decay (without the cutoff at $\underline{\delta}$). Finally, by duality, these results imply that the maximal displacement of supercritical branching random walks conditional on extinction has a similar tail behavior.Comment: 29 page

On the estimation of integrated covariance matrices of high dimensional diffusion processes

We consider the estimation of integrated covariance (ICV) matrices of high dimensional diffusion processes based on high frequency observations. We start by studying the most commonly used estimator, the realized covariance (RCV) matrix. We show that in the high dimensional case when the dimension $p$ and the observation frequency $n$ grow in the same rate, the limiting spectral distribution (LSD) of RCV depends on the covolatility process not only through the targeting ICV, but also on how the covolatility process varies in time. We establish a Mar\v{c}enko--Pastur type theorem for weighted sample covariance matrices, based on which we obtain a Mar\v{c}enko--Pastur type theorem for RCV for a class $\mathcal{C}$ of diffusion processes. The results explicitly demonstrate how the time variability of the covolatility process affects the LSD of RCV. We further propose an alternative estimator, the time-variation adjusted realized covariance (TVARCV) matrix. We show that for processes in class $\mathcal {C}$, the TVARCV possesses the desirable property that its LSD depends solely on that of the targeting ICV through the Mar\v{c}enko--Pastur equation, and hence, in particular, the TVARCV can be used to recover the empirical spectral distribution of the ICV by using existing algorithms.Comment: Published in at http://dx.doi.org/10.1214/11-AOS939 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Discrete Fractal Dimensions of the Ranges of Random Walks in Zd\Z^d Associate with Random Conductances

Let X= {X_t, t \ge 0} be a continuous time random walk in an environment of i.i.d. random conductances {\mu_e \in [1, \infty), e \in E_d}, where E_d is the set of nonoriented nearest neighbor bonds on the Euclidean lattice Z^d and d\ge 3. Let R = {x \in Z^d: X_t = x for some t \ge 0} be the range of X. It is proved that, for almost every realization of the environment, dim_H (R) = dim_P (R) = 2 almost surely, where dim_H and dim_P denote respectively the discrete Hausdorff and packing dimension. Furthermore, given any set A \subseteq Z^d, a criterion for A to be hit by X_t for arbitrarily large t>0 is given in terms of dim_H(A). Similar results for Bouchoud's trap model in Z^d (d \ge 3) are also proven

Occupation Statistics of Critical Branching Random Walks in Two or Higher Dimensions

Consider a critical nearest neighbor branching random walk on the $d$-dimensional integer lattice initiated by a single particle at the origin. Let $G_{n}$ be the event that the branching random walk survives to generation $n$. We obtain limit theorems conditional on the event $G_{n}$ for a variety of occupation statistics: (1) Let $V_{n}$ be the maximal number of particles at a single site at time $n$. If the offspring distribution has finite $\alpha$th moment for some integer $\alpha\geq 2$, then in dimensions 3 and higher, $V_n=O_p(n^{1/\alpha})$; and if the offspring distribution has an exponentially decaying tail, then $V_n=O_p(\log n)$ in dimensions 3 and higher, and $V_n=O_p((\log n)^2)$ in dimension 2. Furthermore, if the offspring distribution is non-degenerate then $P(V_n\geq \delta \log n | G_{n})\to 1$ for some $\delta >0$. (2) Let $M_{n} (j)$ be the number of multiplicity-$j$ sites in the $n$th generation, that is, sites occupied by exactly $j$ particles. In dimensions 3 and higher, the random variables $M_{n} (j)/n$ converge jointly to multiples of an exponential random variable. (3) In dimension 2, the number of particles at a "typical" site (that is, at the location of a randomly chosen particle of the $n$th generation) is of order $O_p(\log n)$, and the number of occupied sites is $O_p(n/\log n)$

The random conductance model with Cauchy tails

We consider a random walk in an i.i.d. Cauchy-tailed conductances environment. We obtain a quenched functional CLT for the suitably rescaled random walk, and, as a key step in the arguments, we improve the local limit theorem for $p^{\omega}_{n^2t}(0,y)$ in [Ann. Probab. (2009). To appear], Theorem 5.14, to a result which gives uniform convergence for $p^{\omega}_{n^2t}(x,y)$ for all $x,y$ in a ball.Comment: Published in at http://dx.doi.org/10.1214/09-AAP638 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Statistical Properties of Microstructure Noise

We study the estimation of moments and joint moments of microstructure noise. Estimators of arbitrary order of (joint) moments are provided, for which we establish consistency as well as central limit theorems. In particular, we provide estimators of auto-covariances and auto-correlations of the noise. Simulation studies demonstrate excellent performance of our estimators even in the presence of jumps and irregular observation times. Empirical studies reveal (moderate) positive auto-correlation of the noise for the stocks tested