Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Veraart, Almut"'
This article surveys key aspects of ambit stochastics and remembers Ole E. Barndorff-Nielsen's important contributions to the foundation and advancement of this new research field over the last two decades. It also highlights some of the emerging tre
Externí odkaz:
http://arxiv.org/abs/2410.00566
Autor:
Leonte, Dan, Veraart, Almut E. D.
We consider trawl processes, which are stationary and infinitely divisible stochastic processes and can describe a wide range of statistical properties, such as heavy tails and long memory. In this paper, we develop the first likelihood-based methodo
Externí odkaz:
http://arxiv.org/abs/2308.16092
The aim of this paper is to develop estimation and inference methods for the drift parameters of multivariate L\'evy-driven continuous-time autoregressive processes of order $p\in\mathbb{N}$. Starting from a continuous-time observation of the process
Externí odkaz:
http://arxiv.org/abs/2307.13020
Autor:
Veraart, Almut E. D.
This article introduces the class of periodic trawl processes, which are continuous-time, infinitely divisible, stationary stochastic processes, that allow for periodicity and flexible forms of their serial correlation, including both short- and long
Externí odkaz:
http://arxiv.org/abs/2303.04121
In this paper, we conduct a systematic large-scale analysis of order book-driven predictability in high-frequency returns by leveraging deep learning techniques. First, we introduce a new and robust representation of the order book, the volume repres
Externí odkaz:
http://arxiv.org/abs/2211.13777
Autor:
Sauri, Orimar, Veraart, Almut E. D.
Trawl processes belong to the class of continuous-time, strictly stationary, infinitely divisible processes; they are defined as L\'{e}vy bases evaluated over deterministic trawl sets. This article presents the first nonparametric estimator of the tr
Externí odkaz:
http://arxiv.org/abs/2209.05894
Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persi
Externí odkaz:
http://arxiv.org/abs/2208.10724
Autor:
Leonte, Dan, Veraart, Almut E. D.
Trawl processes are continuous-time, stationary and infinitely divisible processes which can describe a wide range of possible serial correlation patterns in data. In this paper, we introduce new simulation algorithms for trawl processes with monoton
Externí odkaz:
http://arxiv.org/abs/2208.08784
A feasible central limit theorem for realised covariation of SPDEs in the context of functional data
This article establishes an asymptotic theory for volatility estimation in an infinite-dimensional setting. We consider mild solutions of semilinear stochastic partial differential equations and derive a stable central limit theorem for the semigroup
Externí odkaz:
http://arxiv.org/abs/2205.03927
In this article we will introduce the realised semicovariance for Brownian semistationary (BSS) processes, which is obtained from the decomposition of the realised covariance matrix into components based on the signs of the returns, and study its in-
Externí odkaz:
http://arxiv.org/abs/2111.02366