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The tutorials will take place on Friday the 15th of December 2017 and the introductory CRoNoS Winter Course on Dependence models will take place the 13-14 December 2017. The registration for the tutorials will take place in the same building. The number of participants to the tutorials is limited and restricted only to those who attend the conference. For further information send an email to

Participants will be expected to have their own laptop with the latest versions of R and the R packages copula, mvtnorm, nor1mix, qrmtools, qrng, MASS, bbmle, latticeExtra, xts, npcp, rugarch, copulaData and lattice installed.

A link with some material will be provided to the students

Programme - Friday, 15th of December 2017
  • TUTORIAL 1: 9:00-13:30 (coffee break at 11:00)

    Title: Dealing with non-stationarity, serial dependence and ties in copula inference
    Prof. Marius Hofert, University of Waterloo, Canada.
    Prof. Ivan Kojadinovic, University of Pau, France.
    Email: Contact

  • Although it is stand-alone, this tutorial can be seen as the last module of the winter course. It will start by an overview of copula theory and related statistical inference, and will then address more advanced topics such as the handling of non-stationarity, serial dependence, filtering and ties. All the presented concepts will be illustrated by reproducible R examples involving either synthetic or real data.

  • TUTORIAL 2: 15:00 - 19:30 (coffee break at 17:00)

    Title: Tail Dependence with Copulas
    Prof. Fabrizio Durante, University of Salento, Italy.

    Email: Contact

  • Copula models have showed several advantages in describing the behavior of a multivariate stochastic system (e.g., a risk portfolio) because of their flexibility in describing various dependence aspects. In particular, from a risk management perspective, special care should be devoted to the description of the dependence in the tails of the joint distribution function.
    Here we focus on some selected investigations about tail dependence (as described by means of copulas) and its possible applications. Our aim is to provide some theoretical, computational and graphical tools that may help the decision maker in the correct identification of linkages among different random variables, especially in a risky scenario.