Stephan is a PhD candidate in Actuarial Science at Cass Business School in his final year.
His research is mainly in statistical methodology for actuarial reserving but the forecasting problems on which he is working can also be found in mortality forecasting, biostatistics and many other applications.
His work ranges from non-parametric statistics, survival analysis and computational statistics to the specific application in reserving for general (non-life) insurance.
At the moment, he is working on different continuous extensions of the chain-ladder method involving kernel density and kernel hazard estimators. This involves various tasks from asymptotic theory to implementation of new methods and their application to real data sets as well as in simulation studies.
chain-ladder method, counting processes, general reserving, in-sample forecasting, local polynomial kernel estimation, machine learning, non-parametric statistics, operational time, smooth backfitting, survival analysis.
- MSc in Mathematics, Heidelberg University, Germany
- BSc in Mathematics, Heidelberg University, Germany
English, French and German.
Title of thesis: Estimating operational time in a survival analysis setting and application in actuarial reserving
Summary of research
The aim of my research is to add operational time, i.e., a certain dependence stucture, to the continuous chain ladder model. There is empirical evidence that such a generalisation of the continuous chain ladder model is appropriate and that independence should not be assumed. The goal is to derive full asymptotics for our new estimators and to compare it with another already existing one. Besides, we are interested in the results on small datasets as they can be found in practice.
Furthermore, I examine and compare the performance of simpler, multiplicative models for small samples sizes in simulations and on a dataset.
- Professor Jens Perch, Professor of Actuarial Science
- Dr Munir Hiabu, Research Fellow
- Mammen, E. Heidelberg.
Journal articles (3)
- Bischofberger, S.M. (2020). In-Sample Hazard Forecasting Based on Survival Models with Operational Time. Risks, 8(1), pp. 3–3. doi:10.3390/risks8010003.
- Bischofberger, S.M., Hiabu, M., Mammen, E. and Nielsen, J.P. (2019). A comparison of in-sample forecasting methods. Computational Statistics and Data Analysis, 137, pp. 133–154. doi:10.1016/j.csda.2019.02.009.
- Bischofberger, S.M., Hiabu, M. and Isakson, A. (2019). Continuous chain-ladder with paid data. Scandinavian Actuarial Journal. doi:10.1080/03461238.2019.1694973.
- Bischofberger, S., Hiabu, M., Mammen, E. and Nielsen, J.P. Smooth backfitting for additively structured hazard rates.