Amirkabir University of TechnologyAUT Journal of Mathematics and Computing2783-2449Articles in Press20231031A distributionally robust approach for the risk-parity portfolio selection problem526910.22060/ajmc.2023.22260.1145ENM.BayatDepartment of Mathematics and Computer Science,
Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranFarnazHooshmandDepartment of Mathematics and Computer Science,
Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran0000-0002-2449-3925Seyyed AliMirHassaniDepartment of Mathematics and Computer Science,
Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran0000-0002-9894-7053Journal Article20230309Risk-parity is one of the most recent and interesting strategies in the portfolio selection area. Considering the mean-standard-deviation risk measure, this paper studies the risk-parity problem under the uncertainty of the covariance matrix. Assuming that the uncertainty is represented by a finite set of scenarios, the problem is formulated as a scenario-based stochastic programming model. Then, since the occurrence probabilities of scenarios are not known with certainty, two ambiguity sets of distributions are considered, and corresponding to each one, a distributionally robust optimization model is presented. Computational experiments on real-world instances taken from the literature confirm the importance of the proposed models in terms of stability, volatility and Sharpe-ratio.