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This paper develops a stochastic model to assess storm risk in Austria, which relates wind speed and actual losses. By virtue of a building-stock-value-weighted wind index, we use suitably normalised historical loss data of residential buildings over 12 years and corresponding wind speed data to calibrate the model. Subsequently, additional wind speed data is used to generate further scenarios and to obtain loss curves for storm risk that give rise to storm insurance loss quantiles and corresponding solvency capital requirements both on the aggregate and on the regional level. We also investigate the diversification effect across regions and use tools from extreme value theory to assess the insurability of storm risk in Austria in general.

In this paper, we review and discuss some challenges in insuring flood risk in Europe on the national level, including high correlation of damages. Making use of recent advances in extreme value theory, we, furthermore, model flood risk with heavy-tailed distributions and their truncated counterparts and apply the discussed techniques to an inflation-and building-value-adjusted annual data set of flood losses in Europe. The analysis leads to Value-at-Risk estimates for individual countries and for Europe as a whole, allowing to quantify the diversification potential for flood risk in Europe. Finally, we identify optimal risk pooling possibilities in case a joint insurance strategy on the European level cannot be realized and quantify the resulting inefficiency in terms of additional necessary solvency capital. Thus, the results also contribute to the ongoing discussion on how public risk transfer mechanisms can supplement missing private insurance coverage. Keywords Flood risk Á Europe Á Risk pooling Á Risk transfer mechanism Á EVT techniques Á Value at Risk (VaR) Á Expected shortfall (ES) Á Joint risk pooling initiatives (JRPIs) Á Solvency II

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