This paper argues that traditional cyber risk classifications are too restrictive for effective out-of-sample forecasting. It recommends focusing on dynamic, impact-based classifications for better predictions of cyber risk losses, suggesting that risk types are more useful for modeling event frequency rather than severity.
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Cyber risk classifications often fail in out-of-sample forecasting despite their in-sample fit. Dynamic, impact-based classifiers...
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