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See detailAsset Pricing under Rational Learning about Rare Disasters
Koulovatianos, Christos UL; Wieland, Volker

Scientific Conference (2017, January 07)

Why is investment in stocks so persistently weak after a rare disaster? Connecting disaster episodes with post-disaster expectations seems crucial for such post-disaster forecasting and also policymaking ... [more ▼]

Why is investment in stocks so persistently weak after a rare disaster? Connecting disaster episodes with post-disaster expectations seems crucial for such post-disaster forecasting and also policymaking, but rational-expectations models with variable disaster risk often fail to achieve this connection. To this end, while retaining full rationality, we introduce limited information and learning about rare-disaster risk and show that the resulting stock-investment behavior seems similar to persistent investor fear after a rare disaster. We study, (a) rational learning for state verification (RLS), with investors knowing the data-generating process of disaster riskiness but being unable to observe whether the economy is in a riskier state (regime) or not, and (b) rational learning about the data-generating process (RLP) of disaster risk, with investors also being unaware of the data-generating process of disaster riskiness. We analytically show that both RLS and RLP synchronize disaster events with post-disaster expectations and asset prices, and create persistence in price-dividend ratios even if data-generating processes of disaster risk have no persistence. Using De Finetti's theorem we show that RLP offers an explanation for global spells of pessimism and weak investment after a disaster. [less ▲]

Detailed reference viewed: 139 (5 UL)
See detailAsset Pricing under Rational Learning about Rare Disasters
Koulovatianos, Christos UL; Wieland, Volker

Report (2011)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 122 (17 UL)