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Atef Lechiheb

Publications and source records attributed to Atef Lechiheb.

12 recordsLinked to original sources

A CIR-Type Diffusion Driven by Hermite Processes: Well-Posedness, Positivity and Malliavin Analysis

We study a generalized Cox--Ingersoll--Ross (CIR) diffusion \begin{equation*} dX_t = a\bigl(b(t)-X_t\bigr)\,dt + \bigl(\sigma_0+\sigma_1\sqrt{\phi_\eps(X_t)}\bigr)\,dZ_t^{(q,H)}, \quad X_0 = x_0 > 0, \end{equation*} where $Z^{(q,H)}$ is a Hermite process of order $q\geq 1$ and Hurst parameter $H\in(1/2,1)$, and $\phi_\eps$ is a smooth regularisation of the square root. This framework simultaneously captures long-range dependence, non-Gaussian innovations (for $q\geq 2$), and the positivity of the classical CIR model. Since Hermite processes with $q\geq 2$ are not semimartingales, we interpret the dynamics pathwise in the Young--Stieltjes sense, exploiting the H\"{o}lder regularity of $Z^{(q,H)}$. We establish four main results. First, well-posedness: a unique strong solution exists in a fractional Sobolev space, under globally Lipschitz coefficients (satisfied by $\phi_\eps$). Second, a quantitative positivity bound: for $\sigma_1=0$, the probability that $X$ stays positive on $[0,T]$ is bounded below by an explicit expression tending to $1$ as the initial level and long-run target grow large relative to $\sigma_0$; an almost-sure statement, available in the classical Brownian case, is not established here, since the usual boundary-non-attainment mechanism relies on tools unavailable for a non-semimartingale driver. Third, Malliavin differentiability: $X_t\in\D^{1,\infty}$, with an explicit formula for the Malliavin derivative as the solution of a linearised Young SDE. Fourth, absolute continuity: the law of $X_t$ is absolutely continuous with respect to the Lebesgue measure for all $t>0$.

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Higher-Order Multifractional Stable Motion: Definition and Fundamental Properties

This paper introduces the $n$-th order multifractional stable motion ($n$-MFSM), a novel stochastic process that simultaneously unifies three key modelling features: heavy-tailed distributions ($\alpha$-stable with $\alpha\in(1,2]$), time-varying local regularity via a functional Hurst parameter $H(t)\in(n-1,n)$, and extended scaling behaviour of order $n\geq1$. No existing framework combines all three. We establish rigorous existence via $L^\alpha$-integrability analysis, derive both moving-average and harmonizable representations with explicit constants, prove local asymptotic self-similarity with complete identification of the limit process, determine the exact pointwise H\"older regularity $\alpha_X(t)=H(t)-1/\alpha$, and characterize long-range dependence through codifference asymptotics. In particular, we obtain the precise decay exponent $(\alpha-1)H_+ + H(s)-n$ and the LRD criterion $(\alpha-1)H_++H(s)<n$, which generalizes the classical condition $H(s)+H_+<1$ for first-order Gaussian multifractional processes and reduces to $\alpha H-1$ for LFSM with constant $H$.

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Fractional Navier-Stokes Equations with Caputo Derivative Driven by Hermite Noise

We study time-fractional stochastic Navier-Stokes equations on a bounded domain of $\R^2$ (the restriction to dimension two is essential for the bilinear estimates via Sobolev embeddings) driven by a Hermite process $Z_H^k$ of order $k\ge1$ and Hurst parameter $H\in(1/2,1)$. This class of noises generalizes fractional Brownian motion ($k=1$) and the Rosenblatt process ($k=2$). We construct the Wiener integral with respect to $Z_H^k$ and establish sharp $L^p$ estimates via hypercontractivity, explicitly capturing the dependence on $k$. Using a refined Hilbert-Schmidt estimate for the Mittag-Leffler operator, we prove that the stochastic convolution belongs to $\dot{H}^\nu$ under the condition $\al(1-\nu)+2H>2$. A fixed-point argument in a weighted space yields the existence, uniqueness, and H\"older regularity of mild solutions. We also prove a non-central limit theorem linking the solution to discrete approximations.

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Stochastic Burgers equation driven by multiplicative Rosenblatt noise: local existence, uniqueness and regularity

We study the stochastic Burgers equation driven by a multiplicative Rosenblatt noise with Hurst parameter $H \in (1/2,1)$. Using a fixed-point argument in a Malliavin--Sobolev space that controls the solution and its first two Malliavin derivatives, we prove local existence and uniqueness of a mild solution. We establish uniform moment bounds of all orders and prove H\"older regularity: spatial H\"older exponent $\gamma < 1/2$ and temporal H\"older exponent $\alpha < H-1/2$, which are shown to be sharp by a lower bound for the linearized equation. The proof relies on sharp estimates of the heat kernel in the reproducing kernel Hilbert space $\cH$ of the Rosenblatt process, on Meyer's inequalities for moment bounds, and on a careful analysis of the Skorohod integral with respect to the Rosenblatt process. These results provide a rigorous foundation for the study of nonlinear SPDEs driven by non-Gaussian long-memory noise.

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Concentration of Truncated Signatures of Gaussian Rough Paths

This paper establishes a comprehensive concentration theory for truncated signatures of Gaussian rough paths. The signature of a path, defined as the collection of all iterated integrals, provides a complete description of its geometric structure and has emerged as a powerful tool in machine learning and stochastic analysis. Despite growing applications in finance, healthcare, and engineering, the non-asymptotic concentration properties of signature features remain largely unexplored. We prove that level-$k$ signature coordinates exhibit optimal $\exp(-c t^{2/k})$ tail decay and establish dimension-free concentration inequalities for the full truncated signature vector. Our results reveal a fundamental trade-off: higher truncation levels capture more complex path properties but exhibit heavier tails. For Brownian motion and fractional Brownian motion with Hurst parameter $H > 1/4$, we derive explicit variance formulas and sharp constants. The technical contributions combine rough path theory with Gaussian analysis, leveraging Wiener chaos decomposition, hypercontractivity, and the algebraic structure of tensor algebras. We further establish concentration for log-signatures, lead-lag transformations, and provide sample complexity bounds for statistical learning with signature features. This work bridges advanced probability theory with practical applications, offering both theoretical guarantees and computational methods for signature-based approaches in sequential data analysis.

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Fractional Homogenization of Parabolic Equations with Long-Range Random Potentials

This paper establishes a complete homogenization theory for the one-dimensional parabolic equation with long-range correlated random potential: \[ \partial_t u_\varepsilon(t,x) = \frac{1}{2} \partial_{xx} u_\varepsilon(t,x) + \varepsilon^{-\alpha/2} a\left(\frac{x}{\varepsilon}\right) u_\varepsilon(t,x), \] where the random field $a$ has covariance decaying as $|x|^{-\alpha}$ with $\alpha \in (0,1)$. Contrary to classical homogenization where rapid decorrelation leads to deterministic limits, the non-integrable covariance preserves macroscopic randomness. We prove that under the critical scaling $\varepsilon^{-\alpha/2}$, the solution converges in distribution to a stochastic limit described by a fractional Gaussian field with Hurst index $H = 1-\alpha/2 > 1/2$: \[ u(t,x) = \mathbb{E}^B\left[\varphi(x+B_t) \exp\left(\beta\int_{\mathbb{R}} L_t^x(y) dW^H(y)\right)\right], \] where $W^H$ is fractional Brownian motion and the integral is a Young integral. Our contributions include: (i) functional convergence of the integrated potential to fBm, (ii) quantitative convergence rates in Wasserstein distance $W_2(u_\varepsilon, u) \leq C\varepsilon^{\min(\alpha,1-\alpha)/4}$, (iii) a central limit theorem for rescaled fluctuations with scaling $\varepsilon^{-\alpha/4}$, and (iv) superdiffusive transport $\mathbb{E}[X_t^2] \sim t^{2H}$. The results reveal a new homogenization mechanism driven by long-range dependence, connecting stochastic homogenization, fractional calculus, and anomalous diffusion theory.

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Canonical Rough Path over Tempered Fractional Brownian Motion: Existence, Construction, and Applications

We construct a canonical geometric rough path over $d$-dimensional tempered fractional Brownian motion (tfBm) for any Hurst parameter $H > 1/4$ and tempering parameter $\lambda > 0$. The main challenge stems from the non-homogeneous nature of the tfBm covariance, which exhibits a power-law structure at small scales and exponential decay at large scales. Our primary contribution is a detailed analysis of this covariance, proving it has finite 2D $\rho$-variation for $\rho = 1/(2H)$. This verifies the criterion of Friz and Victoir, guaranteeing the existence of a rough path lift. We provide an explicit construction of the rough path $\mathbf{B}_{H,\lambda} = (B_{H,\lambda}, \mathbb{B}_{H,\lambda})$ via $L^2$-limits, establishing its basic properties with explicit constants $C(H,\lambda,T)$. As direct consequences, we obtain: (i)~a complete characterisation of integration regimes, with Young integration applicable for $H > 1/2$ and rough path theory necessary and sufficient for $H \in (1/4, 1/2]$; (ii)~the well-posedness of rough differential equations driven by tfBm, together with a Milstein-type numerical scheme of optimal strong convergence rate $\bigO(n^{-H})$; and (iii)~the foundation for signature calculus for tfBm, including the existence and factorial decay of the signature. The boundary case $H = 1/2$ is treated explicitly, recovering the Stratonovich lift of the Ornstein--Uhlenbeck process and, as $\lambda \to 0^+$, classical It\^o calculus. Numerical experiments confirm the theoretical convergence rates $\bigO(N^{-2H})$ for the L\'evy area approximation and $\bigO(n^{-H})$ for the Milstein scheme. This work provides the first comprehensive pathwise framework for stochastic calculus with tfBm.

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Geometric Rough Paths above Mixed Fractional Brownian Motion

This paper establishes a comprehensive theory of geometric rough paths for mixed fractional Brownian motion (MFBM) and its generalized multi-component extensions. We prove that for a generalized MFBM of the form $M_t^H(a) = \sum_{k=1}^N a_k B_t^{H_k}$ with $\min\{H_k\} > \frac{1}{4}$, there exists a canonical geometric rough path obtained as the limit of smooth rough paths associated with dyadic approximations. This extends the classical result of Coutin and Qian \cite{coutin2002} for single fractional Brownian motion to the mixed case. We provide explicit bounds on the $p$-variation norms and establish a Skorohod integral representation connecting our pathwise construction to the Malliavin calculus framework. Furthermore, we demonstrate applications to rough differential equations driven by MFBM, enabling the use of Lyons' universal limit theorem for this class of processes. Finally, we study the signature of MFBM paths, providing a complete algebraic characterization of their geometric properties. Our approach unifies the treatment of multiple fractional components and reveals the fundamental interactions between different regularity scales, completing the rough path foundation for mixed fractional processes with applications in stochastic analysis and beyond.

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Stochastic Burgers Equation Driven by a Hermite Sheet with Additive Noise: Existence, Uniqueness, and Regularity

We study the stochastic Burgers equation driven by an additive Hermite sheet of order $q \ge 1$. The equation is formulated in the mild sense using the heat semigroup, and existence and uniqueness of solutions are established via a fixed-point argument in suitable Banach spaces. Under appropriate conditions on the Hurst parameters of the Hermite sheet, we derive uniform moment estimates for the solution, which form the basis for the regularity analysis. We prove that the solution admits a continuous modification that is H\"older continuous in both time and space, with exponents determined by the Hurst parameters of the driving noise. In addition, we show that the solution inherits an anisotropic self-similarity property from the Hermite sheet, and we identify the corresponding scaling exponents. The additive noise structure allows the stochastic convolution to be defined through multiple Wiener--It\^o integrals with deterministic kernels. As a consequence, the analysis avoids Malliavin calculus techniques that are typically required for non-Gaussian noises of Hermite rank $q \ge 2$.

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Wiener integrals with respect to the two-parameter tempered Hermite random fields

The two-parameter tempered Hermite field modifies the power law kernel in the moving average representation of the Hermite field by adding an exponential tempering. This paper develops the basic theory of two-parameter tempered Hermite field, including moving average, sample path properties, spectral representations and the theory of Wiener stochastic integration with respect to the two-parameter tempered Hermite field of order one.

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Fluctuations in 1D stochastic homogenization of pseudo-elliptic equations with long-range dependent potentials

This paper deals with the homogenization problem of one-dimensional pseudo-elliptic equations with a rapidly varying random potential. The main purpose is to characterize the homogenization error (random fluctuations), i.e., the difference between the random solution and the homogenized solution, which strongly depends on the autocovariance property of the underlying random potential. It is well known that when the random potential has short-range dependence, the rescaled homogenization error converges in distribution to a stochastic integral with respect to standard Brownian motion. Here, we are interested in potentials with long-range dependence and we prove convergence to stochastic integrals with respect to Hermite process.

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Convergence of random oscillatory integrals in the presence of long-range dependence and application to homogenization

This paper deals with the asymptotic behavior of random oscillatory integrals in the presence of long-range dependence. As a byproduct, we solve the corrector problem in random homogenization of one-dimensional elliptic equations with highly oscillatory random coefficients displaying long-range dependence, by proving convergence to stochastic integrals with respect to Hermite processes.

math.PR