Xu, Peng (2024) Testing out-of-sample portfolio performance using second-order stochastic dominance constrained optimization approach. International Review of Financial Analysis, 95. p. 103368. DOI https://doi.org/10.1016/j.irfa.2024.103368
Xu, Peng (2024) Testing out-of-sample portfolio performance using second-order stochastic dominance constrained optimization approach. International Review of Financial Analysis, 95. p. 103368. DOI https://doi.org/10.1016/j.irfa.2024.103368
Xu, Peng (2024) Testing out-of-sample portfolio performance using second-order stochastic dominance constrained optimization approach. International Review of Financial Analysis, 95. p. 103368. DOI https://doi.org/10.1016/j.irfa.2024.103368
Abstract
Second-order Stochastic Dominance (SSD) criterion can be used to support portfolio decision making under risk and uncertainty. In this paper, we develop novel robust SSD criteria to capture the strength of dominance and portfolio optimization models utilizing these criteria to identify portfolios whose in-sample SSD dominance over a given benchmark is likely to hold also out-of-sample. The developed models can incorporate incomplete probability information by allowing a set of feasible state probabilities. We also show that these portfolio optimization models can be formulated as linear programming problems. We report results from applying these SSD-based portfolio optimization models with different sets of state probabilities in an empirical application, with a focus on evaluating the out-of-sample portfolio performance of the optimized portfolios.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Empirical likelihood; Incomplete probability information; Out-of-sample analysis; Portfolio optimization; Robustness; Stochastic dominance |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 04 Jun 2024 09:19 |
Last Modified: | 30 Oct 2024 21:23 |
URI: | http://repository.essex.ac.uk/id/eprint/38461 |
Available files
Filename: IRFA_Xu2024.pdf
Licence: Creative Commons: Attribution 4.0