Higher Order Effects in Asset Pricing Models with Long‐Run Risks
发现最新一代长期风险资产定价模型存在显著非线性，常用的Campbell-Shiller对数线性化会产生较大数值误差，进而导致模型对权益溢价和收益可预测性的预测出现严重偏差。
ABSTRACT This paper shows that the latest generation of asset pricing models with long‐run risk exhibit economically significant nonlinearities, and thus the ubiquitous Campbell‐Shiller log‐linearization can generate large numerical errors. These errors translate in turn to considerable errors in the model predictions, for example, for the magnitude of the equity premium or return predictability. We demonstrate that these nonlinearities arise from the presence of multiple highly persistent processes, which cause the exogenous states to attain values far away from their long‐run means with nonnegligible probability. These extreme values have a significant impact on asset price dynamics.