Robust portfolio optimization: a categorized bibliographic review
Robust portfolio optimization refers to finding an asset allocation strategy whose behavior
under the worst possible realizations of the uncertain inputs, eg, returns and covariances, is …
under the worst possible realizations of the uncertain inputs, eg, returns and covariances, is …
Robust portfolio selection problems: a comprehensive review
A Ghahtarani, A Saif, A Ghasemi - Operational Research, 2022 - Springer
This paper reviews recent advances in robust portfolio selection problems and their
extensions, from both operational research and financial perspectives. A multi-dimensional …
extensions, from both operational research and financial perspectives. A multi-dimensional …
Two-stage robust unit commitment for co-optimized electricity markets: An adaptive data-driven approach for scenario-based uncertainty sets
Two-stage robust unit commitment (RUC) models have been widely used for day-ahead
energy and reserve scheduling under high renewable integration. The current state of the art …
energy and reserve scheduling under high renewable integration. The current state of the art …
Power management in active distribution systems penetrated by photovoltaic inverters: A data-driven robust approach
F Mancilla-David, A Angulo… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Under the smart grid paradigm, distribution systems with large penetrations of photovoltaic-
based power generation are called to optimize their operational resources to achieve a …
based power generation are called to optimize their operational resources to achieve a …
Robust strategic bidding in auction-based markets
In this paper, we propose an alternative methodology for devising revenue-maximizing
strategic bids under uncertainty in the competitors' bidding strategy. We focus on markets …
strategic bids under uncertainty in the competitors' bidding strategy. We focus on markets …
A new data-driven distributionally robust portfolio optimization method based on wasserstein ambiguity set
N Du, Y Liu, Y Liu - IEEE Access, 2020 - ieeexplore.ieee.org
Since optimal portfolio strategy depends heavily on the distribution of uncertain returns, this
article proposes a new method for the portfolio optimization problem with respect to …
article proposes a new method for the portfolio optimization problem with respect to …
Time-consistent risk-constrained dynamic portfolio optimization with transactional costs and time-dependent returns
Dynamic portfolio optimization has a vast literature exploring different simplifications by
virtue of computational tractability of the problem. Previous works provide solution methods …
virtue of computational tractability of the problem. Previous works provide solution methods …
A robust extreme learning machine based on adaptive loss function for regression modeling
F Zhang, S Chen, Z Hong, B Shan, Q Xu - Neural Processing Letters, 2023 - Springer
The extreme learning machine (ELM) algorithm is advantageous to regression modeling
owing to its simple structure, fast computation, and good generalization performance …
owing to its simple structure, fast computation, and good generalization performance …
A novel probabilistic risk measure model for multi-period uncertain portfolio selection
HL Dai, CY Huang, FT Lai, XT Lv, HM Dai, S Tan… - Soft Computing, 2024 - Springer
We systematically study the multi-period uncertain portfolio selection problem when the
security return follows the uncertain distribution assessed by experts. However, the existing …
security return follows the uncertain distribution assessed by experts. However, the existing …
Distributionally robust portfolio optimization with linearized STARR performance measure
We study the distributionally robust linearized stable tail adjusted return ratio (DRLSTARR)
portfolio optimization problem, in which the objective is to maximize the worst-case …
portfolio optimization problem, in which the objective is to maximize the worst-case …