Asset Return Dynamics and Learning
提出一种预期形成理论，融合行为金融动机与理性预期框架，在资产定价模型中让代理人简化预测模型，并让参数和预测变量选择在均衡中共同决定，发现多重均衡和内生切换，校准美国股票数据后能再现收益和波动的体制转换。
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). The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.