Researchers release third version of coherent risk in finite models paper
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Tomasz Kania released the third version of the coherent risk in finite models research paper on the arXiv q-fin section on October 20, 2026, which belongs to the field of quantitative finance risk management. The paper proves that when estimates remain unchanged after replicating all scenarios, binary estimators can accurately describe the same population risk and recover the population dual set from finite models. The study derives Wasserstein, Kolmogorov, and total variation moduli under bounded support based on risk from Bernoulli losses, characterizes uniform estimability, gives impossibility bounds for arbitrary random estimators, and also derives uniform regret bounds for data-driven risk aversion optimization, applied to stop-loss retention scenarios.
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- arXiv q-finCoherent Risk Research under Finite Models
This research in quantitative financial risk management proves that when estimates remain unchanged after replicating all scenarios, binary estimators can accurately describe the same population risk and recover the population dual set from finite models. The study derives Wasserstein, Kolmogorov, and total variation norms under bounded support based on Bernoulli loss risk, characterizes uniform estimability, provides impossibility bounds for arbitrary stochastic estimators, and further derives uniform regret bounds for data-driven risk-averse optimization, with applications in stop-loss retention scenarios.
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