The first YUIMA Summer School on Computational and Statistical Methods for Stochastic Process

This 4 days course aims at introducing researchers, PhD students and practitioners to several aspects of numerical and statistical analysis of time series through the R language and, in particular, the YUIMA package.

The course covers topics of R programming, time series data handling, simulation and numerical analysis for several types of statistical models including: point processes, stochastic differential equations driven by Brownian motion with or without jumps, fractional Brownian motion and Lévy processes.

Stochastic differential equations, with or without jumps, are nowadays used as statistical models in many contexts, including but not limited to, finance, insurance, phylogenetics, genomics, political analysis, economics, migration flow analysis, social network analysis, and more. They are continuous-time models fitted on discrete sampled data. Point processes, like Compound Poisson and Hawkes processes are used in Limit Order Book (LOB) analysis in trading and finance, as well as in the analysis of rainfalls in meteorology or in earthquakes analysis in seismology. Jump and Lèvy processes extends many of the above models to a variety of statistical distributions that are able to capture stylised facts about real time series. Last, but not least, fractional processes are typical tools of climate change studies. Although the course could not cover all of the above applications, the participants can benefit from understanding the simulation and estimation techniques for these classes of stochastic process and applyto their own research field with the help of the faculties of this summer school.

25-28 June 2019, Brixen-Bressanone, Italy

For detailed information see the course page.

Registration closes on May 20th 2019!