The article explores the benefits of using robust optimization in minimizing downside risk measures like Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) in the Indian market. By extending the robust optimization framework, the researchers found that robust models outperformed their base versions when dealing with a higher number of stocks and simulated setups. This suggests that incorporating robust optimization can lead to superior performance in managing risk in the market.