Does Smooth Ambiguity Matter for Asset Pricing?
用贝叶斯方法估计包含平滑模糊偏好的消费资产定价模型，基于市场与消费数据，发现含模糊性、学习和时变波动率的模型优于长期风险模型。
We use the Bayesian method introduced by Gallant and McCulloch (2009) to estimate consumption-based asset pricing models featuring smooth ambiguity preferences. We rely on semi-nonparametric estimation of a flexible auxiliary model in our structural estimation. Based on the market and aggregate consumption data, our estimation provides statistical support for asset pricing models with smooth ambiguity. Statistical model comparison shows that models with ambiguity, learning, and time-varying volatility are preferred to the long-run risk model. We also analyze asset pricing implications of the estimated models. Received April 12, 2016; editorial decision September 11, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online