This dissertation consists of four essays exploring risk modeling and aggregation, with a particular focus on cyber risk in the insurance context. It aims to provide appropriate answers to research questions that arise from practical challenges on both the supply and demand sides of the...

The effects of temporal aggregation and choice of sampling frequency are of great interest in modeling the dynamics of asset price volatility. We show how the squared low-frequency returns can be expressed in terms of the temporal aggregation of a high-frequency series. Based on the theory of...

We compare direct forecasts of HICP and HICP excluding energy and food in the euro area and five member countries to aggregated forecasts of their main components from large Bayesian VARs with a shared set of predictors. We focus on conditional point and density forecasts, in line with...

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of...

The current Energy Market is not yet ready for the integration of the Smart Grid context. Concepts such as Demand Response and Distributed Generation, namely renewable energy resources, are not yet included in current business models in order to the system flow properly. Therefore, the authors...

Industrial demand response can play an important part in balancing the intermittent production from a growing share of renewable energies in electricity markets. This paper analyses the role of aggregators - intermediaries between participants and the electricity market - in facilitating...

In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries' predictive GDP growth densities, taking into account cross-country interdependencies. Speci?cally, we...