Forecasting Agricultural Financial Weather Risk Using PCA and SSA in an Index Insurance Model in Low-Income Economies

This article presents a novel methodology to assess the financial risk crops in highly weather-volatile regions. We use data-driven methodologies that singular value decomposition techniques low-income economy. The measure is first derived by applying frameworks, Principal Component Analysis (PCA), and Singular Spectrum (SSA) productive coffee Colombia (163 weather stations) during 2010–2019. objective understand future implications index insurance tools will have on strategic economic country. stage includes identification of PCA components at country level. measure, payouts-in-exceedance ratio, or POER, from an analysis most volatile-weather-producing It obtained linear model applied extracted singular-decomposed tendencies through SSA first-component data. due volatilities serves predict both seasons—wet dry. results show component contributes forty percent total variance. seasonal forecast for next 24 months shows increasing additional payouts (PO), especially wet season. caused average precipitation tendency with POERs 18 60 second years. findings provide important insights into designing agricultural hedging instruments economies are reliant export crops, as case Colombian coffee.