Portfolio optimization and out-of-sample performance

• Autores: Francisco J. Nogales
• Localización: XXXI Congreso Nacional de Estadística e Investigación Operativa ; V Jornadas de Estadística Pública: Murcia, 10-13 de febrero de 2009 : Libro de Actas, 2009, ISBN 978-84-691-8159-1
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • In this work, we study the practical performance of one of the most known optimization problems in Finance: the Portfolio Selection Problem. This is an optimization problem with uncertainty, coming from unknown future asset returns. To alleviate the impact of estimation error on this problem, several approaches have been recently proposed.

    Among the best approaches are: those based on ignoring the expected asset returns (imposing a factor structure on the associated covariance matrix; shrinking the sample covariance matrix; adding short-sale constraints; constraining the portfolio norm), those based on optimizing the worst-case performance (robust portfolio optimization) and the naive 1/N rule.

    We compare empirically (in terms of portfolio variance, Sharpe ratio, value-at-risk, and turnover) the out-of-sample performance of these strategies across several real datasets.

    From these results, we give some insights on how to improve practical performance of proposed policies.