Research on Financial Risk Assessment and Asset Allocation Optimization Based on Monte Carlo Simulation and Markowitz Theory

This paper explores the use of a combined approach of Markowitz portfolio theory and Monte Carlo simulations for optimizing asset allocation and risk management in financial markets. First, we introduce the portfolio optimization framework of the Markowitz theory, focusing on how to construct an efficient portfolio by minimizing risk and maximizing expected return. Next, by integrating the Monte Carlo simulation method, the paper simulates asset returns and risk fluctuations under different market scenarios, aiming to assess the potential risks of various asset portfolios. To make the model more realistic, we assume an efficient market, rational investors, and neglect transaction costs and market frictions. Additionally, we consider the covariance matrix between assets, assuming its stability during the analysis period. Finally, based on a single-period investment model, the paper proposes an optimized asset allocation strategy, offering a more scientifically grounded decision-making approach for investors. The results show that combining these two methods can effectively improve the accuracy of asset allocation and reduce the overall risk of the portfolio. This study provides a new perspective and practical solution for financial risk assessment and asset management.