3 résultats pour « distortion risk measures »

Pareto‑Optimal Peer‑to‑Peer Risk Sharing with Robust Distortion Risk Measures

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The paper explores Pareto optimality in decentralized peer-to-peer risk-sharing markets using robust distortion risk measures. It characterizes optimal risk allocations, influenced by agents' tail risk assessments. Using flood risk insurance as an example, the study compares decentralized and centralized market structures, highlighting benefits and drawbacks of decentralized insurance.
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An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models

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“ In this paper, we propose an efficient important sampling method for distortion risk measures in such models that reduces the computational cost through machine learning. We demonstrate the applicability and efficiency of the Monte Carlo method in numerical experiments on various distortion risk measures and models.”An Integrated App”
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Estimation of Generalized Tail Distortion Risk Measures with Applications in Reinsurance

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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.
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