Asset pricing under rational learning about rare disasters

Arbeitspapier

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: beliefs, Bayesian learning, controlled diffusions and jump processes, learning about jumps, adaptive learning, rational learning.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IMFS Working Paper Series ; No. 46

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Search; Learning; Information and Knowledge; Communication; Belief; Unawareness ; Portfolio Choice; Investment Decisions ; Bayesian Analysis: General ; Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making‡ ; Macroeconomics: Consumption; Saving; Wealth ; Criteria for Decision-Making under Risk and Uncertainty ; Optimization Techniques; Programming Models; Dynamic Analysis
• Thema: beliefs ; Bayesian learning ; controlled diffusions and jump processes ; learning about jumps ; adaptive learning ; rational learning

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Koulovatianos, Christos ; Wieland, Volker
• Ereignis: Veröffentlichung
• (wer): Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)
• (wo): Frankfurt a. M.
• (wann): 2011

Weitere Informationen

• Handle: http://hdl.handle.net/10419/97775
• URN: urn:nbn:de:hebis:30-115309
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ