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On the Size Distribution of Macroeconomic Disasters

Econometrica 2011 79(5), 1567-1589
The coefficient of relative risk aversion is a key parameter for analyses of behavior toward risk, but good estimates of this parameter do not exist. A promising place for reliable estimation is rare macroeconomic disasters, which have a major influence on the equity premium. The premium depends on the probability and size distribution of disasters, gauged by proportionate declines in per capita consumption or gross domestic product. Long-term national-accounts data for 36 countries provide a large sample of disasters of magnitude 10% or more. A power-law density provides a good fit to the size distribution, and the upper-tail exponent, α, is estimated to be around 4. A higher α signifies a thinner tail and, therefore, a lower equity premium, whereas a higher coefficient of relative risk aversion, γ, implies a higher premium. The premium is finite if α > γ. The observed premium of 5% generates an estimated γ close to 3, with a 95% confidence interval of 2 to 4. The results are robust to uncertainty about the values of the disaster probability and the equity premium, and can accommodate seemingly paradoxical situations in which the equity premium may appear to be infinite.

Real(istic) Time-Varying Probability of Consumption Disasters

Journal of Financial and Quantitative Analysis 2026 61(2), 906-940
We model the time-varying probability of consumption disasters with international risk interactions and estimate the model using national accounts data of 42 countries back to 1833. The estimated world and country-specific disaster probabilities accord well with historical macroeconomic disasters. A match of the equity premium requires a relative risk aversion coefficient of approximately 5, which is significantly lower than previous estimates. Furthermore, the model provides notably better fits for equity volatility compared with alternative rare-disaster models. Finally, the disaster probability index estimated from the model demonstrates significant out-of-sample predictive power over long horizons, performing well not only over time but also across countries.