The purpose of this paper is to assess the losses of weather‐related insurance at different regional levels. The possibility of spatial diversification of insurance is explored by estimating the joint occurrence on unfavorable weather conditions in different locations, looking particularly at the tail behavior of the loss distribution.
Joint weather‐related losses are estimated using copulas. Copulas avoid the direct estimation of multivariate distributions but allow for much greater flexibility in modeling the dependence structure of weather risks compared with simple correlation coefficients.
Results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a large regional scale. Thus the possibility to reduce risk exposure by increasing the trading area of insurance is limited.
The empirical findings are limited by a rather weak database. In that case the estimation of high‐dimensional copulas leads to large estimation errors.
The paper includes implications for the quantification of systemic weather risk which is important for the rate making of crop insurance and reinsurance.
This paper's results highlight how important the choice of the statistical approach is when modeling the dependence structure of weather risks.
