Index for Predicting Insurance Claims from Wind Storms with an Application in France

Abstract

For insurance companies, wind storms represent a main source of volatility, leading to potentially huge aggregated claim amounts. In this article, we compare different constructions of a storm index allowing us to assess the economic impact of storms on an insurance portfolio by exploiting information from historical wind speed data. Contrary to historical insurance portfolio data, meteorological variables show fewer nonstationarities between years and are easily available with long observation records; hence, they represent a valuable source of additional information for insurers if the relation between observations of claims and wind speeds can be revealed. Since standard correlation measures between raw wind speeds and insurance claims are weak, a storm index focusing on high wind speeds can afford better information. A storm index approach has been applied to yearly aggregated claim amounts in Germany with promising results. Using historical meteorological and insurance data, we assess the consistency of the proposed index constructions with respect to various parameters and weights. Moreover, we are able to place the major insurance events since 1998 on a broader horizon beyond 40 years. Our approach provides a meteorological justification for calculating the return periods of extreme-storm-related insurance events whose magnitude has rarely been reached.

Modeling asymptotically independent spatial extremes based on Laplace random fields

“…We illustrate modeling on daily maximum wind gust data from the Netherlands collected from 14/11/1999 to 13/11/2008 for 30 meteorological stations, available for download from the Royal Netherlands Meteorological Institute (www.knmi.nl). Modeling extreme wind gusts is important for applications like insurance risk (Brodin and Rootzén, 2009;Mornet et al, 2015), forest damage (Pontailler et al, 1997;Dhôte, 2005;Nagel et al, 2006) or wind farming (Seguro and Lambert, 2000;Steinkohl et al, 2013). Recent studies on similar data (Engelke et al, 2015;Einmahl et al, 2015;Oesting et al, 2015) are based on max-stable models without challenging the assumption of asymptotic dependence.…”

Analysis and Evaluation Research on Road Damage of Post-Earthquake Using Generalized Information Diffusion Model

“…2. It can be expressed in this form: The square root of the ratio between the average distance from the sample point to the stub axis and the average value of the long axis [21,22,23,24].…”

Cascade Sensitivity Measures

“…However, in this stream of literature, sensitivity measures generally do not account explicitly for the impact of the dependence between input factors. This matters, given that the impact of (stochastic) dependencies is a persistent topic in risk modelling; see for example Mornet et al (2015), Su et al (2015), Wang et al (2016),…”

“…However, in this stream of literature, sensitivity measures generally do not account explicitly for the impact of the dependence between input factors. This matters, given that the impact of (stochastic) dependencies is a persistent topic in risk modelling; see, for example, Mornet, Opitz, Luzi, and Loisel (2015)), Su, Mahadevan, Xu, and Deng (2015), Wang, Dyer, and Butler (2016), and Werner, Bedford, and Quigley (2018). Hence, we are concerned with reflecting the impact of the dependence between input factors in sensitivity metrics.…”