期刊
JOURNAL OF EMPIRICAL FINANCE
卷 63, 期 -, 页码 252-269出版社
ELSEVIER
DOI: 10.1016/j.jempfin.2021.07.009
关键词
Stock returns forecasting; Factor model; Large data sets; Forecast evaluation
By utilizing a high number of macroeconomic predictors through large dimensional factor models, we show that the Generalized Dynamic Factor Model aids in predicting equity premium. Additionally, accurate predictions can be achieved by combining rolling and recursive forecasts in real-time.
We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well-known factor model with a static representation of the common components with the Generalized Dynamic Factor Model, which accounts for time series dependence in the common components. Using statistical and economic evaluation criteria, we empirically show that the Generalized Dynamic Factor Model helps predicting the equity premium. Exploiting the link between business cycle and return predictability, we find accurate predictions also by combining rolling and recursive forecasts in real-time.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据