Introduces a robust max-min benchmark for aggregating calibrated forecasts that is LP-tractable, dominates OIH, and is attained by online algorithms under forecast-only feedback.
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A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.
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Robust Aggregation of Calibrated Forecasts
Introduces a robust max-min benchmark for aggregating calibrated forecasts that is LP-tractable, dominates OIH, and is attained by online algorithms under forecast-only feedback.
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Generating Plausible Stress Scenarios via Large Deviations
A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.