Modelling extremal stock returns in a stable Paretian environment

This thesis explores the challenges finance experts and statisticians face in understanding extreme movements in stock prices. It evaluates two main approaches: one based on full parametric assumptions for tail inference and the other allowing the data to reveal insights about the tails. The stable Paretian distribution is utilized as a conceptual framework for this analysis, providing a structured method to investigate and interpret these extremal behaviors in financial data.