An average model approach to experience based premium rates discounts: an application to Spanish agricultural insurance

Vilar-Zanón, José L.; Heras, Antonio; Frutos, Estela de

doi:10.1007/s13385-020-00234-1

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…In this research, we employ successive regression and quantile regression models to investigate whether climate change influences hailstorm risk at both the expectation and high quantile levels of certain insurance-relevant variables, such as the monthly number of claims, loss costs equal to one, and total losses. Regression is a crucial methodology in insurance rate making (see, for instance, Lemaire 2012;Denuit et al 2007;De Jong and Heller 2008;and Vilar-Zanón et al 2020 for posterior rate making in agricultural insurance). Quantile regression is a non-parametric methodology that extends the idea of regression to quantiles and traces back to Koenker and Bassett (1978).…”

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confidence: 99%

Impact Assessment of Climate Change on Hailstorm Risk in Spanish Wine Grape Crop Insurance: Insights from Linear and Quantile Regressions

Zhou,

Vilar-Zanón

2024

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There is growing concern that climate change poses a serious threat to the sustainability of the insurance business. Understanding whether climate warming is a cause for an increase in claims and losses, and how this cause–effect relationship will develop in the future, are two significant open questions. In this article, we answer both questions by particularizing the geographical area of Spain, and a precise risk, hailstorm in crop insurance in the line of business of wine grapes. We quantify climate change using the Spanish Actuarial Climate Index (SACI). We utilize a database containing all the claims resulting from hail risk in Spain from 1990 to 2022. With homogenized data, we consider as dependent variables the monthly number of claims, the monthly number of loss costs equal to one, and the monthly total losses. The independent variable is the monthly Spanish Actuarial Climate Index (SACI). We attempt to explain the former through the latter using regression and quantile regression models. Our main finding is that climate change, as measured by the SACI, explains these three dependent variables. We also provide an estimate of the increase in the monthly total losses’ Value at Risk, corresponding to a future increase in climate change measured in units of the SACI. Spanish crop insurance managers should carefully consider these conclusions in their decision-making process to ensure the sustainability of this line of business in the future.

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