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Learning with rare disasters
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AI
DOI：10.3982/QE1716.png)
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摘要
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Financial crises appear to have long-lasting effects, even after the crisis itself has passed. This paper offers a simple explanation based on Bayesian learning from rare events. Agents face a latent and time-varying probability of economic disaster. When a disaster occurs, learning results in greater effects on asset prices because agents update their probability of future disasters. Moreover, agents' belief that the disaster risk is high can rationally persist for years, even when it is in fact low. We generalize the model to allow for a noisy signal of the disaster probability. This generalized model explains excess stock market volatility together with negative skewness, effects that previous models in the literature struggle to explain.
Keyword:
Negative skewness
financial crises
jumps
G12
期刊
Q
IF:
2.2
论文数:
24
被引数:
0
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