We introduce a simulation scheme for Brownian semistationary processes, which is based
on discretizing the stochastic integral representation of the process in the time domain. We …

Since we will never really know why the prices of financial assets move, we should at least
make a faithful model of how they move. This was the motivation of Bachelier in 1900, when …

Ambit Stochastics has emerged as a new field in probability theory during the last decade.
While there are still many open questions and challenges, we think that the time is right to …

The design and development of sub-Terahertz (sub-THz) cellular systems entail the need for
new channel models that can precisely predict channel characteristics beyond 100GHz in …

We introduce the notion of relative volatility/intermittency and demonstrate how relative
volatility statistics can be used to estimate consistently the temporal variation of …

Developing accurate and computationally efficient channel models for mobile wireless
channels poses a formidable challenge, primarily due to the highly dynamic nature of such …

The design and development of Terahertz (THz) and sub-Terahertz (sub-THz)
communication systems entail the need for new channel models that can precisely predict …

The simulation of vehicular wireless channels using geometry-based radio channel models
is computationally intensive when the number of scatterers is significantly high. In this paper …

This article studies the finite sample behaviour of a number of estimators for the integrated
power volatility process of a Brownian semistationary process in the non semi-martingale …

Stable convergence is a type of convergence of random variables, which is stronger than
weak convergence but weaker than convergence in probability. It has been used in …