TY - JOUR
ID - 5269
TI - A distributionally robust approach for the risk-parity portfolio selection problem
JO - AUT Journal of Mathematics and Computing
JA - AJMC
LA - en
SN - 2783-2449
AU - Bayat, M.
AU - Hooshmand, Farnaz
AU - MirHassani, Seyyed Ali
AD - Department of Mathematics and Computer Science,
Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
Y1 - 2023
PY - 2023
VL -
IS -
SP -
EP -
KW - Portfolio selection problem
KW - Risk-parity
KW - Scenario-based stochastic model
KW - Distributionally robust
KW - Ambiguity sets
DO - 10.22060/ajmc.2023.22260.1145
N2 - 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.
UR - https://ajmc.aut.ac.ir/article_5269.html
L1 -
ER -