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Tailored to the extremes: Quantile regression for index‐based insurance contract design
Author(s) -
Conradt Sarah,
Finger Robert,
Bokusheva Raushan
Publication year - 2015
Publication title -
agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.29
H-Index - 82
eISSN - 1574-0862
pISSN - 0169-5150
DOI - 10.1111/agec.12180
Subject(s) - quantile regression , index (typography) , ordinary least squares , econometrics , expected shortfall , actuarial science , economics , yield (engineering) , statistics , quantile , dependency (uml) , risk management , mathematics , computer science , finance , materials science , world wide web , metallurgy , software engineering
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.