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Stocks with high uncertainty about risk, as measured by the volatility of expected volatility (vol-of-vol), robustly underperform stocks with low uncertainty about risk by 8% per year. This vol-of-vol effect is distinct from (combinations of) at least 20 previously documented return predictors, survives many robustness checks, and holds in the United States and across European stock markets. We empirically explore the pricing mechanism behind the vol-of-vol effect. The evidence points toward preference-based explanations and away from alternative explanations. Collectively, our results show that uncertainty about risk is highly relevant for stock prices.

Experiments frequently use a random incentive system (RIS), where only tasks that are randomly selected at the end of the experiment are for real. The most common type pays every subject one out of her multiple tasks (within-subjects randomization). Recently, another type has become popular, where a subset of subjects is randomly selected, and only these subjects receive one real payment (betweensubjects randomization). In earlier tests with simple, static tasks, RISs performed well. The present study investigates RISs in a more complex, dynamic choice experiment. We find that between-subjects randomization reduces risk aversion. While within-subjects randomization delivers unbiased measurements of risk aversion, it does not eliminate carry-over effects from previous tasks. Both types generate an increase in subjects' error rates. These results suggest that caution is warranted when applying RISs to more complex and dynamic tasks.

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