This thesis explores the growing complexity of contemporary financial markets, which is a consequence of a world that is increasingly interconnected and correlated. This evolution highlights the necessity of understanding and accurately modeling these underlying relationships, which translates into the need of incorporating more complex models into portfolio optimization, breaking away from Harry Markowitz’s foundational Portfolio Optimization Theory. While Markowitz’s model has been effective, the complexity of modern financial instruments demands more sophisticated approaches. This study focuses on the application of copulas and vine models to portfolio optimization, aiming to understand how these advanced models can enhance the optimization process by accurately capturing dependencies among financial assets. In particular, this thesis investigates the benefits of integrating copula-GARCH models, a combination of time series modelling where the residuals are modelled using copulas or vine models, into portfolio theory. Through this approach, the research aims to extend existing knowledge and highlight the specific advantages provided by these models in portfolio optimization.