Applied Mathematics
Vol.06 No.13(2015), Article ID:61608,6 pages
10.4236/am.2015.613194
Strong Local Non-Determinism of Sub-Fractional Brownian Motion
Nana Luan
School of Insurance and Economics, University of International Business and Economics, Beijing, China

Copyright © 2015 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).


Received 30 August 2015; accepted 27 November 2015; published 30 November 2015

ABSTRACT
Let
be a subfractional Brownian motion in
. We prove that
is strongly locally nondeterministic.
Keywords:
Sub-Fractional Brownian Motion, Fractional Brownian Motion, Self-Similar Gaussian Processes, Strong Local Non-Determinism

1. Introduction
The fractional Brownian motion (fBm for short) is the best known and most used process with long-dependence property for models in telecommunications, turbulence, image processing and finance. This process is first introduced by [1] and later studied by [2] . The self-similarity and stationarity of the increments are two main properties for which fBm enjoy success as a modeling tool. The fBm is the only continuous Gaussian process which is self-similar and has stationary increments; see [3] . Many authors have also proposed for using more general self-similar Gaussian processes and random fields as stochastic models; see e.g. [4] -[9] . Such applications have raised many interesting theoretical questions about self-similar Gaussian processes and fields in general. However, in contrast to the extensive studies on fractional Brownian motion, there has been little systematic investigation on other self-similar Gaussian processes until [10] fills the gap by developing systematic ways to study sample path properties of a class of self-similar Gaussian process, namely, the bifractional Brownian motion. Their main tools are the Lamperti transformation, which provides a powerful connection between self-similar processes and stationary processes; see [11] , and the strong local non-determinism of Gaussian processes; see [12] . In particular, for any self-similar Gaussian processes
, the Lamperti transformation leads to a stochastic integal representation for X.
An extension of Bm which preserves many properties of the fBm, but not the stationarity of the increments, is so called sub-fractional Brownian motion (sub-fBm, in short) introduced by [13] . The sub-fBm is another class of self-similar Gaussian process which has properties analogous to those of fBm; see [13] -[15] . Given a constant
, the sub-fractional Brownian motion in
is a centered Gaussian process
with covariance function
(1)
and
.
Let
be independent copies of
. We define the Gaussian process
with values in
by
(2)
By (1), one can verify easily that
is a self-similar process with index H, that is, for every constant

where 

The strong local non-determinism is an important tool to study the sample path properties of self-similar Gaussian process, such as the small ball probability and Chung’s law of the iterated logarithm. In this paper, we apply the Lamperti transformation to prove the strong local non-determinism of

2. Strong Local Non-Determinism
Theorem 1. For all constants









Proof. By Lamperti’s transformation (see [11] ), we consider the centered stationary Gaussian process 

The covariance function 

where 





Using Bochner’s theorem, 

where W is a complex Gaussian measure with control measure 



Since





We would like to prove that f has the following asymptotic property

where 
In the following we give a direct proof of (10) by using (9) and an Abelian argument similar to that in the proof of Theorem 1 of [16] . Without loss of generality, we assume that

with

We need to distinguish three cases:





We will also make use of the properties of higher order derivatives of



and 


The behavior of the derivatives of 


and

Hence, we have




When


Now, we proceed to prove (10). First, we consider the case when

Hence,

Let 

On the other hand, integration-by-parts yields

By Riemann-Lebesgue lemma,

Moreover, since 





Then for all 

Hence, we have

Combining (18), (19), and (24), we have

Then we see that, when

Secondly, we consider the case



Using (20) and integration-by-parts again we derive

It follows from the (27), (16) and Riemann-Lebesgue lemma that

We see from the above and (17) that

This verifies that (10) holds when
Finally we consider the case

Note that we have




It follows from (10) and Lemma 1 of [17] (see also [12] for more general results) that 








Now we prove the strong local nondeterminism of 






Hence, it follows from (31) and (32) that for all 


where
Funding
Supported by NSFC (No. 11201068) and “The Fundamental Research Funds for the Central Universities” in UIBE (No. 14YQ07).
Cite this paper
NanaLuan, (2015) Strong Local Non-Determinism of Sub-Fractional Brownian Motion. Applied Mathematics,06,2211-2216. doi: 10.4236/am.2015.613194
References
- 1. Kolmogorov, A.N. (1940) Wienersche Spiralen und einige andere interessante Kurven im Hilbertschen Raum. C.R. (Doklady) Acad. Sci. URSS (N.S.), 26, 115-118.
- 2. Mandelbrot, B. and van Ness, J.W. (1968) Fractional Brownian Motions, Fractional Noises and Applications. SIAM Review, 10, 422-437.
http://dx.doi.org/10.1137/1010093 - 3. Samorodnitsky, G. and Taqqu, M.S. (1994) Stable Non-Gaussian Random Processes. Stochastic Models with Infinite Variance. Stochastic Modeling. Chapman & Hall, New York.
- 4. Anh, V.V., Angulo, J.M. and Ruiz-Medina, M.D. (1999) Possible Long-Range Dependence in Fractional Random Fields. Journal of Statistical Planning and Inference, 80, 95-110.
http://dx.doi.org/10.1016/S0378-3758(98)00244-4 - 5. Benassi, A., Bertrand, P., Cohen, S. and Istas, J. (2000) Identification of the Hurst Index of a Step Fractional Brownian Motion. Statistical Inference for Stochastic Processes, 3, 101-111.
http://dx.doi.org/10.1023/A:1009997729317 - 6. Benson, D.A., Meerschaert, M.M. and Baeumer, B. (2006) Aquifer Operator-Scaling and the Effect on Solute Mixing and Dispersion. Water Resources Research, 42, W01415.
http://dx.doi.org/10.1029/2004wr003755 - 7. Bonami, A. and Estrade, A. (2003) Anisotropic Analysis of Some Gaussian Models. Journal of Fourier Analysis and Applications, 9, 215-236.
http://dx.doi.org/10.1007/s00041-003-0012-2 - 8. Cheridito, P., Kawaguchi, H. and Maejima, M. (2003) Fractional Ornstein-Uhlenbeck Processes. Electronic Journal of Probability, 8, 14 p.
- 9. Mannersalo, P. and Norros, I. (2002) A Most Probable Path Approach to Queueing Systems with General Gaussian Input. Computer Network, 40, 399-412.
http://dx.doi.org/10.1016/S1389-1286(02)00302-X - 10. Tudor, C.A. and Xiao, Y. (2007) Sample Path Properties of Bifractional Brownian Motion. Bernoulli, 13, 1023-1052.
http://dx.doi.org/10.3150/07-BEJ6110 - 11. Lamperti, J. (1962) Semi-Stable Stochastic Processes. Transactions of the American Mathematical Society, 104, 62-78.
http://dx.doi.org/10.1090/S0002-9947-1962-0138128-7 - 12. Xiao, Y. (2007) Strong Local Non-Determinism of Gaussian Random Fields and Its Applications. In: Lai, T.-L., Shao, Q.-M. and Qian, L., Eds., Asymptotic Theory in Probability and Statistics with Applications, Higher Education Press, Beijing, 136-176.
- 13. Bojdecki, T., Gorostiza, L.G. and Talarczyk, A. (2004) Sub-Fractional Brownian Motion and Its Relation to Occupation Times. Statistics and Probability Letters, 69, 405-419.
http://dx.doi.org/10.1016/j.spl.2004.06.035 - 14. Dzhaparidze, K. and Van Zanten, H. (2004) A Series Expansion of Fractional Brownian Motion. Probability Theory and Related Fields, 103, 39-55.
http://dx.doi.org/10.1007/s00440-003-0310-2 - 15. Tudor, C. (2007) Some Properties of the Sub-Fractional Brownian Motion. Stochastics, 79, 431-448.
http://dx.doi.org/10.1080/17442500601100331 - 16. Pitman, E.J.G. (1968) On the Behavior of the Characteristic Function of a Probability Distribution in the Neighborhood of the Origin. Journal of the Australian Mathematical Society, 8 423-443.
http://dx.doi.org/10.1017/S1446788700006121 - 17. Cuzick, J. and Du Preez, J.P. (1982) Joint Continuity of Gaussian Local Times. Annals of Probability, 10, 810-817.
http://dx.doi.org/10.1214/aop/1176993789

