Nos derniers articles

This paper introduces a robust method for evaluating Conditional Value-at-Risk (CVaR) when data distribution can't be simulated. Using rolling data windows as proxies for independent samples, the approach effectively assesses worst-case risk. Applied to Danish fire insurance data, it outperformed traditional DRO (distributional risk optimization) methods—achieving accurate, less conservative estimates in 87% of cases. This advancement enables reliable risk management even with limited tail data. Future research will focus on refining robustness guarantees and integrating extreme value theory into decision-making models involving rare but impactful events.

New estimators for generalized tail distortion (GTD) risk measures are proposed, based on first-order asymptotic expansions, offering simplicity and comparable or better performance than existing methods. A reinsurance premium principle using GTD risk measure is tested on car insurance claims data, suggesting its effectiveness in embedding safety loading in pricing to counter statistical uncertainty.