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Evidence-based and comprehensible health information is a key element of evidence-based medicine and public health. The goal is informed decision-making based on realistic estimations of health risks and accurate expectations about benefits and harms of interventions. In Germany, standards of evidence-based risk information were poorly followed during the COVID-19 pandemic. Frequently, public information was biased, fragmentary and misleading. Pandemic-related threat scenarios induced emotional distress and unnecessary anxiety. A systematic and comprehensive evaluation of the pandemic measures is crucial, but still pending in Germany. A critical analysis of risk communication by experts, politicians and the media during the pandemic should be a key element of the evaluation process. Evaluation of decision making and media reporting during the pandemic should improve preparedness for future crises.
Gradient capital allocation, also known as Euler allocation, is a technique used to redistribute diversified capital requirements among different segments of a portfolio. The method is commonly employed to identify dominant risks, assessing the risk-adjusted profitability of segments, and installing limit systems. However, capital allocation can be misleading in all these applications because it only accounts for the current portfolio composition and ignores how diversification effects may change with a portfolio restructuring. This paper proposes enhancing the gradient capital allocation by adding “orthogonal convexity scenarios” (OCS). OCS identify risk concentrations that potentially drive portfolio risk and become relevant after restructuring. OCS have strong ties with principal component analysis (PCA), but they are a more general concept and compatible with common empirical patterns of risk drivers being fat-tailed and increasingly dependent in market downturns. We illustrate possible applications of OCS in terms of risk communication and risk limits.