In the context of climate change, severe weather, climate and water events are becoming more intense and frequent. Similarly, cyber threats’ frequency is increasing. Moreover, in a world which is becoming more populated, complex and interconnected, the impacts of climate and cyber events are growing. Therefore, for risk managers and insurers, an accurate quantitative assessment of corresponding risks is more important than ever for appropriate decision-making processes.

Extreme-value theory offers a proper statistical framework to model impactful events as those described above, so knowing at least the basics in this field is an asset for actuaries. Furthermore, for the applications mentioned above and many others, dependence plays a critical role. For instance, extreme rainfall will have a much bigger impact if it affects a whole catchment rather than just a locality, and so knowing to which extent rainfall can be extreme at several locations at the same is crucial. Thus, the main tools allowing a proper quantification and modelling of dependence should be known by actuaries. Understanding the potential limitations of the employed models as well as the consequences of their misuse in concrete cases is also important.

This Summer School of the Swiss Association of Actuaries 2023 will have a particular focus on climate and cyber risks and the statistical models and tools required for their proper assessment. The lectures will be supplemented by several practical sessions where the participants will apply the learned statistical techniques to concrete climate and cyber datasets using the R programming language.

Throughout the Summer School, the following topics will be considered:

• Risk measurement, reporting and monitoring.
• Extreme-value theory (univariate, multivariate, and spatial).
• Dependence measures, multivariate models and copulas.
• Climate change.
• Climate risk.
• Cyber risk.

Upon completion of that course the participants will:

• Understand and master techniques from a general methodological toolkit.
• Be able to apply them for measuring risk in several fields of quantitative risk management.
• Have a precise idea of the modelling challenges associated with climate and cyber risks.
• Be able to use risk management tools available in the R statistical programming language.

Required prerequisites are an intermediate level knowledge of probability and statistics and basic programming skills.