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ABSTRACT
This study explores the concepts of risk and uncertainty in portfolio optimization, introducing Monte Carlo methodology as the investigative approach. The research delves into various types of hazards and highlights the distinction between risk measurement and management in portfolio optimization. Mean-risk decision models are formulated and solved using scenario generation and stochastic programming techniques. The R programming language is utilized to implement the Monte Carlo predictive model, demonstrating its accuracy in optimizing portfolio performance. This research contributes to the understanding of risk and uncertainty in portfolio management, offering insights for informed decision-making.
