A1717
Title: High-dimensional covariance matrix estimators on simulated portfolios
Authors: Andres Garcia - Autonomous University of Baja California (Mexico) [presenting]
Abstract: The allocation of synthetic portfolios under different dependency structures is studied in a high-dimensional context. The research tests approaches based on random matrices, free probability, deterministic equivalents, and their combination with hierarchical clustering. Simulations are compared with the out-of-sample performance of empirical data from the companies that make up the S\&P 500 index, evaluating metrics such as annual return, annual volatility, Sharpe ratio, maximum drawdown, Sortino ratio, and turnover. The portfolio allocation strategies analyzed include the minimum variance portfolio, both with and without short-selling constraints, as well as the hierarchical risk parity approach. The results pave the way for new risk management proposals.
