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Asset Return Dynamics and Learning
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OA
AI
DOI：10.1093/rfs/hhp112.png)
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摘要
En 中文
This article advocates a theory of expectation formation that incorporates many of the central motivations of behavioral finance theory while retaining much of the discipline of the rational expectations approach. We provide a framework in which agents, in an asset pricing model, underparameterize their forecasting model in a spirit similar to Hong, Stein, and Yu (2007) and Barberis, Shleifer, and Vishny (1998), except that the parameters of the forecasting model and the choice of predictor are determined jointly in equilibrium. We show that multiple equilibria can exist even if agents choose only models that maximize (risk-adjusted) expected profits. A real-time learning formulation yields endogenous switching between equilibria. We demonstrate that a real-time learning version of the model, calibrated to U.S. stock data, is capable of reproducing regime-switching returns and volatilities, as recently identified by Guidolin and Timmermann (2007). (JEL G12, G14, D82, D83)
Keyword:
UK STOCK
MODEL
EXPECTATIONS
RISK
CONVERGENCE
VOLATILITY
ALLOCATION
期刊
IF:
5.4
论文数:
2.8K
被引数:
3.0W
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