Project information
SHARPS: Skewness, Higher moments & Alternative Risk measures for Portfolio Selection

Quantifying portfolio risk is one of the biggest challenges that remains fundamental to investment theory and practice, guiding decisions involving uncertain asset returns. While mean-variance frameworks have shaped much of portfolio theory, recent literature emphasizes the importance of downside risk and higher-order moments that accurately reflect investor preferences. In this project, we aim to contribute to this emerging literature by focusing on: i) improving the estimation of semicovariance matrices through novel shrinkage methodologies, ii) investigating the determinants of aggregate portfolio skewness using contemporary co-skewness and multivariate skewness estimators, and iii) developing practical portfolio optimization strategies explicitly designed to capture favorable skewness and reduce downside risk. A distinctive advantage of our research team is our interdisciplinary strength, integrating methodological expertise in downside risk, optimization with applied empirical investigation, and software implementation, promoting broad practical applicability and dissemination.