Resumen de A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms

Victor DeMiguel , Lorenzo Garlappi, Francisco J. Nogales, Raman Uppal

• In this paper, we provide a general nonlinear programming framework for identifying portfolios that have superior out-of-sample performance in the presence of estimation error. This general framework relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold.

  We show that our general framework nests as special cases several wellknown (shrinkage and constrained) approaches considered in the literature. We also use our general framework to propose several new portfolio strategies that we term partial minimum-variance portfolios. These portfolios are obtained by applying the classical conjugate-gradient method to solve the minimum-variance problem.

  We compare empirically (in terms of portfolio variance, Sharpe ratio, and turnover), the out-of-sample performance of the new portfolios to various wellknown strategies across several datasets. We find that the norm-constrained portfolios we propose outperform shortsale-constrained portfolio approaches, shrinkage approaches, the 1/N portfolio, factor portfolios, and also other strategies considered in the literature.