An Erdös–Révész type law of the iterated logarithm for reflected fractional Brownian motion
Let be \textcolorblacka fractional Brownian motion with Hurst parameter . For the stationary storage process , \textcolorblack, we provide a tractable criterion for assessing whether, for any positive, non-decreasing function , equals 0 or 1. Using this criterion we find that, for a family of functions , such that , for some , . Consequently, with , for , and a.s. Complementary, we prove an Erdös–Révész type law of the iterated logarithm lower bound on , i.e., a.s., ; a.s., , where .
Key words and phrases:Extremes of Gaussian fields, storage processes, fractional Brownian motion, law of the iterated logarithm
2010 Mathematics Subject Classification:Primary: 60F15, 60G70; Secondary: 60G22.
1. Introduction and Main Results
The analysis of properties of reflected stochastic processes, being developed in the context of classical Skorokhod problems and \textcolorblacktheir applications to queueing theory, risk theory and financial mathematics, is an actively investigated field of applied probability. In this paper we analyze 0-1 properties of a class of such processes, that due to its importance in queueing theory (and dual risk theory) gained substantial interest; see, e.g., [13, 14, 1, 2] or novel works on -reflected Gaussian processes [7, 12].
Consider a reflected (at 0) fractional Brownian motion with drift , given by the following formula
where and is a fractional Brownian motion (fBm) with Hurst parameter , i.e., a centered Gaussian process with covariance function We focus on the investigation of the long-time behavior of the unique stationary solution of (1), which has the following representation
With no loss of generality in the reminder of \textcolorblackthis paper we assume that the drift parameter . An important stimulus to analyze the distributional properties of and its functionals stems from \textcolorblackthe Gaussian fluid queueing theory, where the stationary buffer content process in a queue which is fed by and emptied with constant rate is described by (2); see e.g. . In particular, in the seminal paper by Hüsler and Piterbarg  the exact asymptotics of one dimensional marginal distributions of was derived; see also [3, 4, 6] for results on more general Gaussian input processes.
The purpose of this paper is to investigate the asymptotic 0-1 behavior of the processes . Our first contribution is an \textcolorblackanalog of the classical finding of Watanabe , where an asymptotic 0-1 type of behavior for centered stationary Gaussian processes was analyzed.
For all functions that are positive and nondecreasing on some interval , it follows that
according as the integral
is finite or infinite.
The exact asymptotics, as grows large, of the probability in \textcolorblackwas found by Piterbarg [14, Theorem 7]. \textcolorblackNamely, for any ,
where , , is the distribution function of \textcolorblackthe unit normal law and the constants are given explicitly in Section 2. Since relation (3) also holds when , provided that , \textcolorblackwe have that for , as ,
Theorem 1 provides a tractable criterion for settling the dichotomy of . For instance, \textcolorblacklet and
One \textcolorblackcan check that, as ,
Hence, for any ,
For any ,
This result extends findings of Zeevi and Glynn [19, Theorem 1], where it was proven that the above convergence holds weakly as well as in for all .
The second contribution of this paper is an Erdös–Révész type of law of the iterated logarithm for the process . We refer to Shao  for more background and references on Erdös–Révész type law of the iterated logarithm and a related result for centered stationary Gaussian processes; \textcolorblacksee also Debicki and Kosiński  for extensions to order statistics.
If , then
If , then
Now, let us complementary put , where
then it follows that
Theorem 2 shows that for big enough, there exists an in (as well as in by (6)) such that and that the length \textcolorblack of the interval is the smallest possible. This shines new light on \textcolorblackresults, which are intrinsically connected with Gumbel limit theorems; see, e.g., , where the function plays crucial role. We shall pursue this elsewhere.
The paper is organized as follows. In Section 2 we introduce some useful properties of storage processes fed by fractional Brownian motion. In Section 3 we provide a collection of basic results on how to interpret extremes of the storage process as extremes of a Gaussian field related to the fractional Brownian motion . Furthermore, \textcolorblackin Section 4 we prove lemmas, which constitute building blocks of the proofs of the main results.
2. Properties of the storage process\textcolor
where, with ,
is a Gaussian field. Note that the self-similarity property of implies that the field has the same distribution for any . Thus, we do not use as an additional parameter in the \textcolorblackfollowing notation whenever it is not needed; \textcolorblacklet . Furthermore, the field is stationary in , but not in . The variance of the field equals and has a single maximum point at
Taylor \textcolorblackexpansion leads to
as , where
Let us define the correlation function of the process as follows
By series expansion we find for any fixed and with ,
provided that and are sufficiently small. For , we have since the increments of Brownian motion on disjoint intervals are independent. Therefore,
for , sufficiently large and some positive constant depending only on , and . Similarly, from (8) it follows that for any fixed there exists such that
for sufficiently small .
Due to the following lemma, while analyzing tail asymptotics of the supremum of , we can restrict the considered domain of to a strip with .
Lemma 1 (Piterbarg , Lemma 2 and 4).
There exists a positive constant such that for any ,
where . Furthermore, for any , with , as ,
is the so-called Pickands’ constant. This holds also for , with .
Hüsler and Piterbarg [9, Corollary 2] showed that the above actually holds true for depending on such that , for any and .
Let and . For a fixed and some , let us define a discretization of the set as follows
Along the same lines as in [10, Lemma 6] we get the following lemma.
There exist positive constants , such that, for any and ,
Finally, it is possible to approximate tail asymptotics of the supremum of on the strip by maximum taken over discrete time points. The proof of the following lemma follows line-by-line \textcolorblackthe same as the proof of [14, Lemma 4] and thus we omit it. Similar result can be found in, e.g., [10, Lemma 7].
For any , as ,
It follows easily that as , so that the above asymptotics \textcolorblackis the same as in Lemma 1 when the discretization parameter decreases to zero so that the number of discretization points grows to infinity.
3. Auxiliary Lemmas
We begin with some auxiliary lemmas that are later needed in the proofs. The first lemma is \textcolorblacka slightly modified version of [11, Theorem 4.2.1].
Lemma 4 (Berman’s inequality).
Suppose \textcolorblackthat are normal \textcolorblackrandom variables with \textcolorblackcorrelation matrix and similarly with \textcolorblackcorrelation matrix . Let \textcolorblack, \textcolorblackand be real numbers, . Then,
The following lemma is a general form of the Borel-Cantelli lemma; cf. .
Lemma 5 (Borel-Cantelli lemma).
Consider a sequence of \textcolorblackevents . If
then . Whereas, if
For any , there exist positive constants and depending only on and such that
for any , with and being some universal positive constant.
Let be some positive constant. For the reminder of the proof let and be two positive constants depending only on and that may differ from line to line. For any put , , , and
From this construction, it is easy to see that the intervals are disjoint. Furthermore, , and , for any and sufficiently large . Note that, for any , as grows large, therefore if is the smallest number of intervals needed to cover , then . Moreover, since is bounded by the constant not depending on and , it follows that, for any .
Now let us introduce a discretization of the set as in Section 2.2. That is, for some , define grid points
Since is an increasing function, it easily follows that,
where the last inequality follows from Berman’s inequality with
Estimate of .
Note that we can use the fact that has the same distribution as for any . Since the process is stationary \textcolorblackwith respect to the first variable, from Lemma 3, for any , sufficiently large and small ,
Estimate of .