Portfolio allocation: a comparison between Hierarchical Risk Parity and Markowitz model in Python

The portfolio allocation is a predominant issue in the world of finance; everyday asset managers from all over the world have to elaborate financial portfolios coherent with the objectives of their clients. The most popular model implemented to solve the portfolio allocation problems derives from the Markowitz’s framework. Over the years, some limitations related to that model have emerged; contextually, new types of financial allocation have been developed, including the Hierarchical Risk Parity. This model aims to address the instability of the quadratic optimizers and it has its roots in the growing interest towards artificial intelligence. In this work a comparison between the Markowitz's framework and the Hierarchical Risk Parity is carried out via Python, through an empirical application on a portfolio of equity ETFs.