On the stability of portfolio selection models

• Published in: Journal of empirical finance 2020-12, Vol.59, p.210-234
• Main Authors: Cesarone, Francesco, Mango, Fabiomassimo, Mottura, Carlo Domenico, Ricci, Jacopo Maria, Tardella, Fabio
• Format: Article
• Language: English
• Subjects: Estimation errors; gain–risk ratio; Portfolio optimization; Profitability analysis; Risk Parity; Stability measures
• ISSN: 0927-5398; 1879-1727
• DOI: 10.1016/j.jempfin.2020.10.003

One of the main issues in portfolio selection models consists in assessing the effect of the estimation errors of the parameters required by the models on the quality of the selected portfolios. Several studies have been devoted to this topic for the minimum variance and for several other minimum risk models. However, no sensitivity analysis seems to have been reported for the recent popular Risk Parity diversification approach, nor for other portfolio selection models requiring maximum gain–risk ratios. Based on artificial and real-world data, we provide here empirical evidence showing that the Risk Parity model is always the most stable one in all the cases analyzed with respect to the portfolio composition. Furthermore, the minimum risk models are typically more stable than the maximum gain–risk models, with the minimum variance model often being the preferable one. The Risk Parity model seems to be the most stable one also with respect to profitability when measured by the Sharpe ratio. However, the maximum gain–risk models, although quite sensitive to the input data, generally appear to attain better profitability results. •Sensitivity of portfolio selection models w.r.t input data perturbation.•Risk Parity is the portfolio selection strategy less sensitive to estimation errors.•Minimum risk portfolio models are typically more stable than gain–risk ratio models.•We study the stability of in-sample and out-of-sample profitability for all models.