Optimizing Financial Risk Control for Multinational Projects: A Joint Framework Based on CVaR-Robust Optimization and Panel Quantile Regression

Based on data from 45 multinational projects in the telecommunications and IT sectors between 2016 and 2022, this study proposes an integrated framework combining Conditional Value-at-Risk (CVaR) robust budget allocation and Panel Quantile Regression (PQR). Uncertainty is modeled using φ-divergence and box-type support sets to ensure distributional robustness. A Financial Risk Exposure Index (FREI) is constructed to capture the transmission of external shocks—such as exchange rate fluctuations and supplier defaults—to project cost deviations. The PQR model identifies the asymmetric impact of FREI across different project quantiles. Empirical findings reveal that a 10% reduction in FREI leads to a 15.8% drop in cost overruns and a 19.4% improvement in on-time delivery. Under a 95% confidence level, CVaR is reduced by 28%, and the budget payback period is shortened by 12.4%. Global sensitivity analysis using Sobol indices further highlights supplier performance volatility and exchange rate variance as the most influential risk drivers. This framework offers a structured, data-driven approach for financial risk mitigation in complex multinational projects and supports more informed resource allocation decisions.