Development and calibration of an economic scenario generator

Insurance companies face a variety of financial and economic risks requiring management. In this context, economic scenario generators (ESGs) are computational tools developed to produce a range of realistic scenarios for financial and economic random variables. These variables, known as risk drivers, influence the risks associated with balance sheet amounts. The strength of an economic scenario generator lies in its ability to produce plausible scenarios for these risk drivers within a specific projection framework. This thesis discusses the development and calibration of an economic scenario generator. Key risk drivers for insurers are presented and modeled, including interest rates, equity prices, real estate prices, and inflation. The thesis details the model selection, calibration, and validation processes, ensuring that the economic scenario generator accurately reflects the real-world dynamics. Both market-consistent and real-world approaches for calibration of models will be discussed.