NORMALIZING RISK MEASURES IN RISK-BASED PORTFOLIOS THROUGH COVARIANCE MISSPECIFICATION ERROR ANALYSIS

This paper focuses on evaluating allocation strategies in portfolio management, specifically examining methods for determining asset weights. The study emphasizes the covariance matrix, a critical component in constructing risk-based portfolios, including minimum volatility, inverse volatility, equal risk contribution, and maximum diversification portfolios. The primary aim is to analyzethe robustness and sensitivity ofthese strategies under potential misspecifications or errors in the covariance matrix. Using a Dynamic Conditional Correlation model and a Monte Carlo simulation approach, a large set of covariance matrices is generated. Risk-based allocation strategies are then applied to these simulated matrices, and robustness is assessed by quantifying deviations between actual and simulated allocations. Furthermore, the study estimates the probability of model accuracy and incorporates this into two conventional risk measures. These adjusted measures account for the risk of covariance misspecification, providing a normalized and more reliable evaluation of portfolio performance. This approach enhances the interpretability and robustness of risk metrics in the presence of estimation errors, offering valuable insights for portfolio optimization under realistic uncertainty conditions.