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In this paper we give explicit examples of power-law correlated stationary Markovian processes y(t) where the stationary pdf shows tails which are gaussian or exponential. These processes are obtained by simply performing a coordinate…

Statistical Mechanics · Physics 2010-12-08 Salvatore Miccichè

We investigate long and short memory in $\alpha$-stable moving averages and max-stable processes with $\alpha$-Fr\'echet marginal distributions. As these processes are heavy-tailed, we rely on the notion of long range dependence suggested…

Probability · Mathematics 2020-06-01 Vitalii Makogin , Marco Oesting , Albert Rapp , Evgeny Spodarev

We consider a continuous-time stochastic volatility model. The model contains a stationary volatility process, the multivariate density of the finite dimensional distributions of which we aim to estimate. We assume that we observe the…

Statistics Theory · Mathematics 2014-07-08 Bert van Es , Peter Spreij

This note aims to give an explicit solution for backward stochastic Volterra integral equations with linear time delayed generators. The process $Y$ is expressed by an integral whose kernel is explicitly given. The processes $Z$ is…

Probability · Mathematics 2021-10-05 Yong Ren , Harouna Coulibaly , Auguste Aman

We prove a contraction in $L^1$ property for the solutions of a nonlinear reaction--diffusion system whose special cases include intercellular transport as well as reversible chemical reactions. Assuming the existence of stationary…

Analysis of PDEs · Mathematics 2008-08-05 M. Chipot , D. Hilhorst , D. Kinderlehrer , M. Olech

This paper is concerned with the evolution dynamics of local times of a spectrally positive stable process in the spatial direction. The main results state that conditioned on the finiteness of the first time at which the local time at zero…

Probability · Mathematics 2024-01-31 Wei Xu

The time fractional ODEs are equivalent to convolutional Volterra integral equations with completely monotone kernels. We therefore introduce the concept of complete monotonicity-preserving ($\mathcal{CM}$-preserving) numerical methods for…

Numerical Analysis · Mathematics 2021-01-01 Lei Li , Dongling Wang

The large deviations of an infinite moving average process with exponentially light tails are very similar to those of an i.i.d. sequence as long as the coefficients decay fast enough. If they do not, the large deviations change…

Probability · Mathematics 2008-02-26 Souvik Ghosh , Gennady Samorodnitsky

In this paper we consider a fractional stochastic volatility model, that is a model in which the volatility may exhibit a long-range dependent or a rough/antipersistent behavior. We propose a dynamic sequential Monte Carlo methodology that…

Methodology · Statistics 2017-02-28 Alexandra Chronopoulou , Konstantinos Spiliopoulos

This study aims to develop the limit theorems on the sample autocovariances and sample autocorrelations for certain stationary infinitely divisible processes. We consider the case where the infinitely divisible process has heavy tail…

Probability · Mathematics 2014-06-17 Takashi Owada

We study the behaviour of the solutions to a dynamic evolution problem for a viscoelastic model with long memory, when the rate of change of the data tends to zero. We prove that a suitably rescaled version of the solutions converges to the…

Analysis of PDEs · Mathematics 2021-06-28 Gianni Dal Maso , Francesco Sapio

In the paper regularity of solutions to stochastic Volterra equations in a separable Hilbert space is studied. Sufficient conditions for the temporal and spatial regularity of stochastic convolutions corresponding to the equations under…

Probability · Mathematics 2012-12-07 Anna Karczewska

We study in this paper the monotonicity properties of the numerical solutions to Volterra integral equations with nonincreasing completely positive kernels on nonuniform meshes. There is a duality between the complete positivity and the…

Numerical Analysis · Mathematics 2023-10-04 Yuanyuan Feng , Lei Li

This article develops a periodic version of a time varying parameter fractional process in the stationary region. It is a partial extension of Hosking (1981)'s article which dealt with the case where the coefficients are invariant in time.…

Statistics Theory · Mathematics 2020-08-06 Amine Amimour , Karima Belaide

We study invariance and monotonicity properties of Kunita-type stochastic differential equations in $\RR^d$ with delay. Our first result provides sufficient conditions for the invariance of closed subsets of $\RR^d$. Then we present a…

Probability · Mathematics 2012-01-06 Igor Chueshov , Michael Scheutzow

Bootstrap methods for estimating the long-run covariance of stationary functional time series are considered. We introduce a versatile bootstrap method that relies on functional principal component analysis, where principal component scores…

Computation · Statistics 2016-10-05 Han Lin Shang

This note develops a stochastic model of asset volatility. The volatility obeys a continuous-time autoregressive equation. Conditions under which the process is asymptotically stationary and possesses long memory are characterised.…

Pricing of Securities · Quantitative Finance 2012-02-28 John A. D. Appleby , John A. Daniels , Katja Krol

We study solutions of the Volterra lattice satisfying the stationary equation for its non-autonomous symmetry. It is shown that the dynamics in $t$ and $n$ are governed by the continuous and discrete Painlev\'e equations, respectively. The…

Exactly Solvable and Integrable Systems · Physics 2019-11-13 V. E. Adler , A. B. Shabat

Let $A$ be a densely defined closed, linear $\omega$-sectorial operator of angle $\theta\in [0,\frac{\pi}{2})$ on a Banach space $X$ for some $\omega\in\mathbb R$. We give an explicit representation (in terms of some special functions) and…

Analysis of PDEs · Mathematics 2016-10-28 Rodrigo Ponce , Mahamadi Warma

Stochastic Volterra equations (SVEs) serve as mathematical models for the time evolutions of random systems with memory effects and irregular behaviour. We introduce neural stochastic Volterra equations as a physics-inspired architecture,…

Machine Learning · Computer Science 2025-12-30 Martin Bergerhausen , David J. Prömel , David Scheffels
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