Research Group "Stochastic Algorithms and Nonparametric Statistics"

Seminar "Modern Methods in Applied Stochastics and Nonparametric Statistics" Winter Semester 2021/2022

09.11.2021 Simon Breneis (Wias Berlin)
Markovian approximations of stochastic Volterra equations with the fractional kernel (online talk)
We consider rough stochastic volatility models where the variance process satisfies a stochastic Volterra equation with the fractional kernel, as in the rough Bergomi and the rough Heston model. In particular, the variance process is therefore not a Markov process or semimartingale, and has quite low Hölder-regularity. In practice, simulating such rough processes thus often results in high computational cost. To remedy this, we study approximations of stochastic Volterra equations using an $N$-dimensional diffusion process defined as solution to a system of ordinary stochastic differential equation. If the coefficients of the stochastic Volterra equation are Lipschitz continuous, we show that these approximations converge strongly with superpolynomial rate in $N$. Finally, we apply this approximation to compute the implied volatility smile of a European call option under the rough Bergomi and the rough Heston model.
16.11.2021 n.n.

23.11.2021 n.n.

30.11.2021 Long-Hao Xu (University of Manchester)
Limiting behavior of the gap between the largest two representative points of statistical distributions (online talk)
The problem of selecting a given number of representative points retaining as much information as possible arises in many situations. It can also be considered as a problem of approximating a continuous distribution by a discrete distribution. In this talk, we are interested in these points reaching the minimum value of mean squared error (we call these points MSE RPs). We illustrate the relationship between MSE RPs and doubly truncated mean residual life (DMRL) as well as mean residual life (MRL), and we discuss the limiting behavior of the gap between the largest two MSE RPs. In simulation studies, we assess the statistical performance of MSE RPs for various distributions in terms of moment estimation and resampling technique. We also discuss the relationship between the tail of the distribution and the gap of MSE RPs.
07.12.2021 Oleg Butkovsky (WIAS Berlin)
Inverting the Markovian projection: A reproducing kernel Hilbert space approach (online talk)
14.12.2021 Uli Sauerland, Anton Benz (Leibniz-Zentrum Allgemeine Sprachwissenschaft)
Numerical challenges in linguistic pragmatics (online talk)
11.01.2022 n.n.

18.01.2022 Yangwen Sun (HU Berlin)
High dimensional change-point detection: A complete graph approach (online talk)
25.01.2022 n. n.
Group meeting
01.02.2022 Alexandra Suvorikova (WIAS Berlin)
Robust k-means claustering in metric spaces (online talk)
In this work we investigate the theoretical properties of robust k-means clustering under assumption of adversarial data corruption. Namely, we provide non-asymptotic rates for excess distortion under weak model assumptions on the moments of the distribution.


22.02.2022 Pavel Dvurechensky (WIAS Berlin)
Hessian barrier algorithms for non-convex conic optimization (online talk)



29.03.2022 William Salkeld (WIAS Berlin)
Lions calculs and mean-field elementary differentials (online talk)
This talk will explore higher order Lions derivatives and provide some applications to the modelling of mean-field dynamic systems.

last reviewed: March 16, 2022 by Christine Schneider