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Foster and Hart propose a measure of riskiness for discrete random variables. Their defining equation has no solution for many common continuous distributions. We show how to extend consistently the definition of riskiness to continuous random variables. For many continuous random variables, the risk measure is equal to the worst‐case risk measure, i.e., the maximal possible loss incurred by that gamble. For many discrete gambles with a large number of values, the Foster–Hart riskiness is close to the maximal loss. We give a simple characterization of gambles whose riskiness is or is close to the maximal loss.

We analyze the Foster-Hart measure of riskiness for general distributions in dynamic settings. The Foster-Hart measure avoids bankruptcy in the long run. It is not time-consistent.

This novel result shows that "worst-case prior" in a setting with drift ambiguity does not always equate to "lowest trend". As a consequence, preemptive pressure reduces. We show that this results in the possibility of firm value being increasing in the level of ambiguity. If only one firm is ambiguous, then the value of the non-ambiguous firm can be increasing in the level of ambiguity of the ambiguous firm.