The paper addresses challenges in risk assessment from limited, non-stationary historical data and heavy-tailed distributions. It introduces a novel method for scaling risk estimators, ensuring robustness and conservative risk assessment. This approach extends time scaling beyond conventional methods, facilitates risk transfers, and enables unbiased estimation in small sample settings. Demonstrated through value-at-risk and expected shortfall estimation examples, the method's effectiveness is supported by an empirical study showcasing its impact.
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