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Analyzing and predicting cat bond premiums: a financial loss premium principle and extreme value modeling (2017)
Journal Article
Stupfler, G., & Yang, F. (2018). Analyzing and predicting cat bond premiums: a financial loss premium principle and extreme value modeling. ASTIN Bulletin, 48(1), https://doi.org/10.1017/asb.2017.32

CAT bonds play an important role in transferring insurance risks to the capital market. It has been observed that typical CAT bond premiums have changed since the recent financial crisis, which has been attributed to market participants being increa... Read More about Analyzing and predicting cat bond premiums: a financial loss premium principle and extreme value modeling.

Estimation of tail risk based on extreme expectiles (2017)
Journal Article
Daouia, A., Girard, S., & Stupfler, G. (in press). Estimation of tail risk based on extreme expectiles. Journal of the Royal Statistical Society: Series B, 80(2), https://doi.org/10.1111/rssb.12254

We use tail expectiles to estimate alternative measures to the Value at Risk (VaR) and Marginal Expected Shortfall (MES), two instruments of risk protection of utmost importance in actuarial science and statistical _nance. The concept of expectiles i... Read More about Estimation of tail risk based on extreme expectiles.

Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions (2017)
Journal Article
El Methni, J., & Stupfler, G. (2018). Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions. Econometrics and Statistics, 6, https://doi.org/10.1016/j.ecosta.2017.03.002

A general way to study the extremes of a random variable is to consider the family of its Wang distortion risk measures. This class of risk measures encompasses several indicators such as the classical quantile/Value-at-Risk, the Tail-Value-at-Risk a... Read More about Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions.