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- W1586556554 abstract "A new approach for weather index-based insurance design based on Quantile Regression (QR) to condition the yield-index dependency is developed and compared to standard regression technique. Three conceptual different risk measures, i.e., Expected Utility, Expected Shortfall and a Spectral Risk Measure, are used to evaluate the risk reducing properties of these contracts. Our findings show that QR is much more powerful in establishing the yield-index dependency and lead for all risk measures to a higher risk reduction than the standard technique ordinary least squares (OLS). Thus, QR leads to a more efficient contract design, which is beneficial for both, the insurer (smaller remaining risk) and the insured (higher demand and willingness to pay). Our empirical application is based on a 31 years long time series of wheat yield data from Northern Kazakhstan." @default.
- W1586556554 created "2016-06-24" @default.
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- W1586556554 date "2015-05-05" @default.
- W1586556554 modified "2023-09-23" @default.
- W1586556554 title "Tailored to the extremes: Quantile regression for index-based insurance contract design" @default.
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- W1586556554 doi "https://doi.org/10.1111/agec.12180" @default.
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