Risk-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.
Bayat, M., Hooshmand, F., & MirHassani, S. A. (2023). A distributionally robust approach for the risk-parity portfolio selection problem. AUT Journal of Mathematics and Computing, (), -. doi: 10.22060/ajmc.2023.22260.1145
MLA
M. Bayat; Farnaz Hooshmand; Seyyed Ali MirHassani. "A distributionally robust approach for the risk-parity portfolio selection problem". AUT Journal of Mathematics and Computing, , , 2023, -. doi: 10.22060/ajmc.2023.22260.1145
HARVARD
Bayat, M., Hooshmand, F., MirHassani, S. A. (2023). 'A distributionally robust approach for the risk-parity portfolio selection problem', AUT Journal of Mathematics and Computing, (), pp. -. doi: 10.22060/ajmc.2023.22260.1145
VANCOUVER
Bayat, M., Hooshmand, F., MirHassani, S. A. A distributionally robust approach for the risk-parity portfolio selection problem. AUT Journal of Mathematics and Computing, 2023; (): -. doi: 10.22060/ajmc.2023.22260.1145