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The literature on time series of functional data has focused on processes of which the probabilistic law is either constant over time or constant up to its second-order structure. Especially for long stretches of data it is desirable to be…

Methodology · Statistics 2020-07-21 Anne van Delft , Michael Eichler

Diffusion processes driven by Fractional Brownian motion (FBM) have often been considered in modeling stock price dynamics in order to capture the long range dependence of stock price observed in reality. Option prices for such models had…

Statistics Theory · Mathematics 2024-05-29 Ananya Lahiri , Rituparna Sen

Fractional Brownian motion is a Gaussian stochastic process with stationary, long-time correlated increments and is frequently used to model anomalous diffusion processes. We study numerically fractional Brownian motion confined to a finite…

Statistical Mechanics · Physics 2019-03-22 T. Guggenberger , G. Pagnini , T. Vojta , R. Metzler

We develop a method that relates the truncated cumulant-function of the fourth order with the L\'evian cumulant-function. This gives us explicit formulas for the L\'evy-parameters, which allow a real-time analysis of the state of a…

Statistical Mechanics · Physics 2019-12-04 Alexander Jurisch

It is well understood that, when numerically simulating SDEs with general noise, achieving a strong convergence rate better than $O(\sqrt{h})$ (where h is the step size) requires the use of certain iterated integrals of Brownian motion,…

Machine Learning · Statistics 2026-01-01 Andraž Jelinčič , Jiajie Tao , William F. Turner , Thomas Cass , James Foster , Hao Ni

Consider the fractional Brownian Motion (fBM) $B^H=\{B^H(t): t \in [0,1] \}$ with Hurst index $H\in (0,1)$. We construct a probability space supporting both $B^H$ and a fully simulatable process $\hat B_{\epsilon}^H $ such that $$\sup_{t\in…

Probability · Mathematics 2019-02-22 Yi Chen , Jing Dong , Hao Ni

Fractional Brownian motion (FBM) is the only Gaussian self-similar process with stationary increments. Its increment process, called fractional Gaussian noise, is ergodic and exhibits a property of power-like decaying autocorrelation…

Statistics Theory · Mathematics 2024-07-10 Michal Balcerek , Krzysztof Burnecki

We consider high-frequency sampled continuous-time autoregressive moving average (CARMA) models driven by finite-variance zero-mean L\'evy processes. An L^2-consistent estimator for the increments of the driving L\'evy process without order…

Probability · Mathematics 2013-02-01 Vincenzo Ferrazzano , Florian Fuchs

Stochastic calculus with respect to fractional Brownian motion (fBm) has attracted a lot of interest in recent years, motivated in particular by applications in finance and Internet traffic modeling. Multifractional Brownian motion (mBm) is…

Probability · Mathematics 2011-03-29 Joachim Lebovits , Jacques Lévy Vehel

Multifractional Brownian motion is an extension of the well-known fractional Brownian motion where the Holder regularity is allowed to vary along the paths. In this paper, two kind of multi-parameter extensions of mBm are studied: one is…

Probability · Mathematics 2007-05-23 E. Herbin

The $d$-dimensional fractional Brownian motion (FBM for short) $B_t=((B_t^{(1)},...,B_t^{(d)}),t\in\mathbb{R})$ with Hurst exponent $\alpha$, $\alpha\in(0,1)$, is a $d$-dimensional centered, self-similar Gaussian process with covariance…

Probability · Mathematics 2009-06-23 Jérémie Unterberger

For $0<\alpha \leq 2$ and $0<H<1$, an $\alpha$-time fractional Brownian motion is an iterated process $Z = \{Z(t)=W(Y(t)), t \ge 0\}$ obtained by taking a fractional Brownian motion $\{W(t), t\in \RR{R} \}$ with Hurst index $0<H<1$ and…

Probability · Mathematics 2011-02-11 Erkan Nane , Dongsheng Wu , Yimin Xiao

We present a method to infer the arbitrary space-dependent drift and diffusion of a nonlinear stochastic model driven by multiplicative fractional Gaussian noise from a single trajectory. Our method, fractional Onsager-Machlup optimisation…

Adaptation and Self-Organizing Systems · Physics 2023-11-07 Johannes A. Kassel , Benjamin Walter , Holger Kantz

Given a sequence of resistance forms that converges with respect to the Gromov-Hausdorff-vague topology and satisfies a uniform volume doubling condition, we show the convergence of corresponding Brownian motions and local times. As a…

Probability · Mathematics 2016-09-08 D. A. Croydon , B. M. Hambly , T. Kumagai

In this paper we investigate the representation of a class of non Gaussian processes, namely generalized grey Brownian motion, in terms of a weighted integral of a stochastic process which is a solution of a certain stochastic differential…

Probability · Mathematics 2019-07-09 Wolfgang Bock , Sascha Desmettre , José Luís da Silva

After a short excursion from discovery of Brownian motion to the Richardson "law of four thirds" in turbulent diffusion, the article introduces the L\'{e}vy flight superdiffusion as a self-similar L\'{e}vy process. The condition of…

Statistical Mechanics · Physics 2015-05-13 A. A. Dubkov , B. Spagnolo , V. V. Uchaikin

This paper is mainly concerned with a kind of fractional stochastic evolution equations driven by L\'evy noise in a bounded domain. We first state the well-posedness of the problem via iterative approximations and energy estimates. Then,…

Probability · Mathematics 2025-01-28 Jiaohui Xu , Tomás Caraballo , José Valero

We investigate first and second order fluctuations of additive functionals of a fractional Brownian motion (fBm) of the form \begin{align}\label{eq:abstractmain} Z_n=\left\{\int_{0}^{t}f(n^{H}(B_{s}-\lambda))ds\ ; t\geq 0 \right\}…

Probability · Mathematics 2021-08-02 Arturo Jaramillo , Ivan Nourdin , David Nualart , Giovanni Peccati

We introduce a general class of stochastic processes driven by a multifractional Brownian motion (mBm) and study the estimation problems of their pointwise H\"older exponents (PHE) based on a new localized generalized quadratic variation…

Mathematical Finance · Quantitative Finance 2018-10-17 Qidi Peng , Ran Zhao

Modelling the behaviour of highly nonlinear dynamical systems with robust uncertainty quantification is a challenging task which typically requires approaches specifically designed to address the problem at hand. We introduce a…

Machine Learning · Statistics 2024-01-25 Thomas Baldwin-McDonald , Mauricio A. Álvarez
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