Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
Robust portfolio selection using interval random programming
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Interval random dependent-chance programming and its application to portfolio selection
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Fuzzy random chance-constrained programming
IEEE Transactions on Fuzzy Systems
Fuzzy random dependent-chance programming
IEEE Transactions on Fuzzy Systems
Hi-index | 12.05 |
This paper addresses a new uncertainty set - interval random uncertainty set for worst-case value-at-risk and robust portfolio optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust worst-case value-at-risk optimization under interval random uncertainty sets in the elements of mean vector and covariance matrix. Numerical experiments with real market data indicate that our approach results in better portfolio performance.