ARC Projects

Econometric modelling of multivariate financial time series  (ARC 07/12-002)

Project description
This interdisciplinary research project deals with modelling, estimation and prediction of the dynamics and the temporal dependence in the mean and the variance-covariance structure of multivariate time series data arising in economic and financial applications. Particular emphasis is put on questions such as dimension reduction (factor approach, modelling of co-movements), non-stationary behaviour over time, modelling of structural breaks (regime-switching), volatilities with and without jump behaviour, etc.
These questions are addressed by a number of econometricians and statisticians using and comparing a series of modern approaches in parametric, semi-parametric and non-parametric statistics. Applications to real data will help to access the quality of the proposed models and estimation procedures.

Semiparametric inference for survival and cure models  (ARC 11/16-039)  

Project description
When modeling time-to-event data, we typically assume that all subjects are at risk and will experience the event of interest if followed long enough.  However, a typical feature of most medical applications is the possibility of "cure", in the sense that some of the subjects will actually not experience the event. Cure models are survival models allowing a cured proportion of individuals. Moreover, measuring times to a certain event in practice naturally induces the presence of right censoring, meaning that one only observes lower bounds for these quantities.  In this project, we study and extend popular semiparametric regression models when the response is possibly right-censored and is allowed to correspond to a non experienced event. Beyond medicine, this type of problems is encountered in a wide variety of fields of applications, like sociology, economy, insurance, ecology, applied sciences, etc. 

Stochastic Modelling of Dependence: Systems under Stress (ARC 12/17-045)

Project description
The project concerns fundamental research on statistical and econometric models for dependence. The aim of the project is to construct new ways of measuring and modelling risks in systems with intricate dependence structures. Particular attention is to be paid to such systems upon the arrival of shocks, after structural breaks, or when comovements between risk factors are higher than usual.