Paridad de riesgo jerárquico: aproximación al método y aplicación para el mercado estadounidense.

This paper presents the Hierarchical Risk Parity (hrp) approach proposed by López de Prado (2016, 2018, 2020) for the construction of optimal investment portfolios using unsupervised learning, hierarchical clustering, which allow overcome some limitations of the Mean-Variance (MV) model, in particular those related to the need to invert the covariance matrix when implementing the cla algorithm. A sample of 7 assets from the American market is taken, with which an application of the hrp algorithm proposed by López de Prado is carried out, finding that under this model the distribution of assets in different clusters generates improvements in terms of the expected return, as well as of the Sharpe coefficient compared to the results of the Mean-Variance portfolio. [ABSTRACT FROM AUTHOR]