Motivated by the progress made towards incorporating robust optimization in the framework of risk minimization, this work focuses on assessing the practical usefulness of the robust optimization approaches for the minimization of downside risk measures, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). Accordingly, we perform empirical analysis of the performance of VaR and CVaR models with respect to their robust counterparts, namely, Worst-Case VaR and Worst-Case CVaR, using both simulated data and market data involving the Indian indices of S&P BSE 30 and S&P BSE 100. Additionally, we provide relevant insights regarding the viability of these robust models over their classical formulations from the perspective of an investment practitioner. We conclude by noting the superior performance of Worst-Case VaR and Worst-Case CVaR with respect to their classical versions in the cases involving higher number of stocks and simulated setup respectively.

受将鲁棒性优化合并到风险最小化框架中所取得的进展的激励，这项工作着重于评估鲁棒性优化方法对最小化下行风险度量（如风险价值（VaR）和条件价值）的实际实用性。 -风险（CVaR）。因此，我们使用涉及标普BSE 30和S＆P印度指数的模拟数据和市场数据，对VaR和CVaR模型相对于其健壮对等物（Worst-Case VaR和Worst-Case CVaR）的性能进行了实证分析。 BSE100。此外，从投资从业者的角度来看，我们提供了有关这些健壮模型在其经典公式上的可行性的相关见解。