Reinsurance Analytics Lecture:

This course provides an introduction to reinsurance from an actuarial perspective. The objective is to understand the fundamentals of risk transfer through reinsurance and the mathematical approaches associated with low frequency high severity events such as natural or man-made catastrophes.

Topics covered include:

• Reinsurance Contracts and Markets: Different forms of reinsurance, their mathematical representation, history of reinsurance, and lines of business.

• Experience Pricing: Modelling of low frequency high severity losses based on historical data, and analytical tools to describe and understand these models

• Exposure Pricing: Loss modelling based on exposure or risk profile information, for both property and casualty risks

• Natural Catastrophe Modelling: History, relevance, structure, and analytical tools used to model natural catastrophes in an insurance context

• Solvency Regulation: Regulatory capital requirements in relation to risks, effects of reinsurance thereon, and differences between the Swiss Solvency Test and Solvency 2

• Insurance linked securities: Alternative risk transfer techniques such as catastrophe bonds 

Lecture Notes 2025 (Excerpt)

Publications:

• with Edoardo Luini:
  Density Estimation of Multivariate Samples using Wasserstein Distance
  Journal of Statistical Computation and Simulation, 88(16), 2019, 3193-3216, publication, preprint, 

• with Mathieu Cambou, Marius Hofert, Christiane Lemieux, Yoshihiro Taniguchi:
  Importance Sampling and Stratification for Copula Models.
  In: Dick J., Kuo F., Woźniakowski H. (Editors) Contemporary Computational Mathematics - A Celebration of the 80th Birthday of Ian Sloan. Springer, 2018, 75-96, preprint, old version

• with Claire Hugo:
  Intelligent Machines - Risks and Opportunities for (Re)insurance
  SCOR technical newsletter #42, 2018, pdf, Presentation, press release

• with William Guevara-Alarcón:
  Piecewise Linear Approximation of Empirical Distributions under a Wasserstein Distance Constraint
  Journal of Statistical Computation and Simulation, 88(16), 2018,
  Preprint, Presentation, Python code, final publication

• with William Guevara-Alarcón:
  Risk Measure Preserving Piecewise Linear Approximation of Empirical Distributions
  European Actuarial Journal, 6(1), 2016, 113-148,
  Preprint, Presentation (Long), Presentation (Short), Code (Python), Code (C++), R Code: (Source, Install Instructions, Windows Binaries, Source on GitHub), Final Publication (pdf), Final Publication (web view)

• with Robert Salzmann:
  On a combination of multiplicative and additive stochastic loss reserving methods
  CAS E-Forum, Summer 2014. Publication, preprint, Presentation, Excel implementation

• Bayesian Copulae Distributions, with Application to Operational Risk Management - Some Comments
  Methodology and Computing in Applied Probability, 15(1), 2013, 105-108. paper on springerlink.com

• Multivariate Modelling in Non-Life Insurance
  Dissertation for the Doctor of Sciences, ETH Zurich, 2012, pdf

• with Christoph Hummel, Georg Mainik:
  Copula based hierarchical risk aggregation through sample reordering
  Insurance: Mathematics and Economics, 51(1), 2012, 122-133.
  preprint, presentation, MatLab example code, R example code, Paper on Convergence (G Mainik),  related Master thesis

• with Davide Canestraro:
  Estimating Copulas for Insurance from Scarce Observations, Expert Opinion and Prior Information: A Bayesian Approach
  ASTIN Bulletin, 42(1), 2012, 271-290. preprint, Presentation, SCOR PrObEx Paper, SCOR PrObEx Leaflet, SCOR PrObEx Summary Chapter  

• with Paul Embrechts, Giovanni Puccetti:
  The GAEP Algorithm for the Fast Computation of the Distribution of a Function of Dependent Random Variables
  Stochastics, 84(5-6), 2012, 569-597. preprint

• with Paul Embrechts, Giovanni Puccetti:
  The AEP algorithm for the fast computation of the distribution of the sum of dependent random variables
  Bernoulli, 17(2), 2011, 562-591. preprint, preprint on arXiv.