Title : Agricultural projects portfolio: A stochastic risk assessment approach
Abstract:
Agricultural financing in development contexts poses unique challenges due to heterogeneous risk profiles, collateral dependency, farmer experience, market volatility, and climatic uncertainty. Traditional credit-evaluation methods typically assess individual loans using deterministic scoring, limiting their ability to capture systemic portfolio risk. This paper introduces a stochastic decision-support framework designed for International. Financial Institutions (IFIs) and agricultural lenders. The framework integrates the Analytic. Hierarchy Process (AHP) to derive evaluation weights (Guarantee Coverage, Profitability, Experience, Financial History) and Monte Carlo simulation to model uncertainty in both criteria weights and project-level performance scores. Portfolio-level optimisation techniques, including the Efficient Frontier and Conditional Value-at-Risk (CVaR), are then applied to balance risk and return across projects. A case study on a portfolio of five agricultural investments demonstrates how institutional lenders can obtain probability distributions of portfolio scores and risk-adjusted allocation weights, rather than static averages. The results show that by modelling uncertainty and interproject correlation, lenders can better balance return and risk, enhance portfolio resilience, and make more data-driven financing decisions. The proposed approach offers a practical tool for agricultural development financing in contexts where project volumes are limited but systemic and climatic risks remain significant.
Keywords: Agricultural credit risk · Multi-criteria decision-making · Stochastic simulation · Portfolio optimisation · CVaR · International Financial Institutions
