Analysis and сomparison of modern portfolio optimization methods in simulated scenarios of economic uncertainty

This bachelor’s thesis focuses on the analysis and comparison of modern portfolio optimization methods, specifically Modern Portfolio Theory, the Black–Litterman model, and dynamic portfolio optimization, within the context of economic uncertainty. The main objective is to evaluate how these methods perform when applied to real market data and subsequently tested in simulated economic scenarios generated through Monte Carlo simulations. The thesis explores the ability of these methods to respond to various market situations, such as stagflation, economic recovery, or a crisis of confidence, and analyzes their advantages and limitations. In addition to the traditional equilibrium-based approach, attention is also given to the flexible framework of dynamic rebalancing. In the practical part, several portfolios optimized according to different strategies are designed and tested, and their performance is quantitatively analyzed across various economic scenarios. The results suggest that none of the methods is universally superior. Their suitability depends on the specific economic environment, investment horizon, and the investor’s risk tolerance. The thesis provides useful practical recommendations for investors and also establishes a theoretical basis for future research in the field of modeling, adaptive strategies, and robust portfolio management in uncertain market conditions.