Working Paper: CEPR ID: DP8514
Authors: Christos Koulovatianos; Volker Wieland
Abstract: This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors' information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the likelihood of rare disasters drop to a much more pessimistic level once a disaster has occurred. Such a shift in beliefs can trigger massive declines in price-dividend ratios. Pessimistic beliefs persist for some time. Thus, belief dynamics are a source of apparent excess volatility relative to a rational expectations benchmark. Due to the low frequency of disasters, even an infinitely-lived investor will remain uncertain about the exact probability. Our analysis is conducted in continuous time and offers closed-form solutions for asset prices. We distinguish between rational and adaptive Bayesian learning. Rational learners account for the possibility of future changes in beliefs in determining their demand for risky assets, while adaptive learners take beliefs as given. Thus, risky assets tend to be lower-valued and price-dividend ratios vary less under adaptive versus rational learning for identical priors.
Keywords: Adaptive Learning; Asset Pricing; Bayesian Learning; Beliefs; Controlled Diffusions; Jump Processes; Learning About Jumps; Rational Learning
JEL Codes: C11; C61; D81; D83; D91; E21; G11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| disaster occurrence (H84) | investor beliefs (G41) |
| investor beliefs (G41) | price-dividend ratios (G35) |
| disaster occurrence (H84) | price-dividend ratios (G35) |
| rational learning (D01) | investor beliefs (G41) |
| adaptive learning (D84) | investor beliefs (G41) |
| investor beliefs (G41) | excess volatility in asset pricing (G19) |