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November 19 – 21, 2018 (WIAS-ESH)
Workshop/Konferenz: Dynamics of Coupled Oscillator Systems
more ... Location
Weierstraß-Institut, Mohrenstr. 39, 10117 Berlin, Erdgeschoss, Erhard-Schmidt-Hörsaal

Host
WIAS Berlin
Tuesday, 20.11.2018, 15:00 (WIAS-406)
Seminar Modern Methods in Applied Stochastics and Nonparametric Statistics
Dr. M. Coghi, WIAS (Berlin):
Pathwise McKean-Vlasov theory
more ... Location
Weierstraß-Institut, Mohrenstr. 39, 10117 Berlin, 4. Etage, Weierstraß-Hörsaal (Raum: 406)

Abstract
We take a pathwise approach to classical McKean-Vlasov stochastic differential equations, as found e.g. in Sznitmann. We are much inspired by Cass-Lyons (and more recent works of Bailleul et al.), but avoid all rough path complications due to our focus on additive noise. The resulting “pathwise McKean-Vlasov theory” is both simple and powerful: as applications we discuss propagation of chaos with a priori independence and exchangeability assumption; common noise and reflecting boundaries are also easy to handle in this framework, last not least we can generalise the Dawson-Gärtner large deviation result to non-Brownian noise.

Host
WIAS Berlin
Wednesday, 21.11.2018, 10:00 (WIAS-406)
Forschungsseminar Mathematische Statistik
Prof. M. Bibinger, Universität Marburg:
Statistical analysis of path properties of volatility
more ... Location
Weierstraß-Institut, Mohrenstr. 39, 10117 Berlin, 4. Etage, Weierstraß-Hörsaal (Raum: 406)

Abstract
In this talk, we review recent contributions on statistical theory to infer path properties of volatility. The interest is in the latent volatility of an It^o semimartingale, the latter being discretely observed over a fixed time horizon. We consider tests to discriminate continuous paths from paths with volatility jumps. Both a local test for jumps at speciied times and a global test for jumps over the whole observation interval are discussed. We establish consistency and optimality properties under infill asymptotics, also for observations with additional additive noise. Recently, there is high interest in the smoothness regularity of the volatility process as confl icting models are proposed in the literature. To address this point, we consider inference on the Hurst exponent of fractional stochastic volatility processes. Even though the regularity of the volatility determines optimal spot volatility estimation methods, forecasting techniques and the volatility persistence, identifiability is an unsolved question in high-frequency statistics. We discuss a first approach which can reveal if path properties are stable over time or changing. Eventually, we discuss some recent considerations and conjectures on this open question. The related easier problem of inference on the Hurst exponent from direct discrete observations of a fractional Brownian motion is also visited.

Host
Humboldt-Universität zu Berlin
Universität Potsdam
WIAS Berlin
Wednesday, 21.11.2018, 10:45 (WIAS-HVP-3.13)
Seminar Interacting Random Systems
W. Sun, TU Berlin:
On the asymptotic distribution of nucleation times of polymerization processes
more ... Location
Weierstraß-Institut, Hausvogteiplatz 11A, 10117 Berlin, 3. Etage, Raum: 3.13

Abstract
Experiments have shown that there is a sharp phase transition in polymerization: it takes long time to have small amount of stable polymers and once some amount of stable polymers appear, very quickly all particles are polymerized. Moreover, the lag time usually have a very high variance. We propose a growth-fragmentation model with a critical mass to explain these phenomenon. Particles having a mass less than this critical mass are unstable -- they are fragmented much more quicker than the larger particles. A scaling approach is used, by taking the initial total mass N as a scaling parameter and assuming that the ratio of the unstable fragmentation rates to stable fragmentation rates are of order Phi(N), which is a non-decreasing function of N. We study the time evolution of this infinite dimension process under a certain class of fragmentation distributions. We show that 1) with a proper scaling parameter, the time (T) spent for the stable polymers that generating from small particles is asymptotically exponential distributed; 2) the time for the growth of stable particles has a much smaller order than T. The exponential distribution explains the high variance and the different time scales explain the sharp phase transition. These results are proved via stochastic calculus, estimations for occupation measures on different time scales, some coupling techniques and branching processes. It is a joint work with Philippe Robert.

Further Informations
Forschungsgruppenseminar Interacting Random System

Host
WIAS Berlin