Modeling the Impact of Geopolitical Shocks on the Efficiency of Letters of Credit A Proposed Framework for Dynamic Simulation and Bayesian Updating at the Central Bank of Libya

Abstract

Foreign trade finance, specifically via Letters of Credit (LCs), constitutes the vital nerve and main supply artery for rentier economies, where the function of this banking instrument transcends being a traditional payment method to become a sensitive indicator of monetary stability. Amidst the state of "Deep Uncertainty" faced by the Libyan economy during the period (2025-2026)—characterized by structural complexities resulting from the intersection of external oil shocks with internal sovereign changes in monetary and fiscal policies—a clear deficiency appeared in traditional linear risk models in providing an accurate explanation for the dynamics of "Economic Waste.

In response to this gap, the study aimed to construct a developed econometric model integrating Stochastic Volatility theory and Bayesian Updating methodology to estimate "Value at Waste" (V).

The study proceeded from the hypothesis that geopolitical risks in the Libyan environment are characterized by "Fat Tails" and sudden "Jumps" that do not follow the Normal Distribution. To achieve this, the study adopted a mixed methodology applied to a monthly time series (January 2025 – January 2026), supported by a programmed simulation algorithm (Python) to process non-linear functions and analyze results via (SPSS).

The research processing led to proving the "Exponential" nature of the inverse relationship between risk and efficiency, confirming that exceeding risk levels beyond a certain point leads to an accelerated exacerbation of economic losses. Results also revealed a "Critical Threshold" for banking resilience; the role of liquidity in absorbing shocks vanishes completely when the risk index exceeds the level of (0.70), at which point monetary surpluses turn into an inflationary burden. Furthermore, the study demonstrated the superiority of the proposed model through "Prediction Gap Analysis," which showed the Bayesian model's success in detecting an estimated economic waste of about $469 million in the crisis month (January 2026), while linear models failed to accommodate this structural break. Based on the foregoing, the study concluded by recommending the necessity for the Central Bank to adopt "Dynamic Simulation" models and approve the "Value at Waste" indicator as a decisive criterion for prioritizing the opening of credits to ensure sustainable food and drug security during crises.