Augmented factor models with applications to validating market risk factors and forecasting bond risk premia
Augmented factor models with applications to validating market risk factors and forecasting bond risk premia
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增强因子模型及其用于验证市场风险因子和预测债券风险溢价的应用
DOI：
10.1016/j.jeconom.2020.07.002
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发表时间：
2021
影响因子：
6.3
通讯作者：
Liao, Yuan
中科院分区：
文献类型：
--
作者：
Fan, Jianqing;Ke, Yuan;Liao, Yuan
我们研究的因素模型增加了观察到的协变量，有解释能力的未知因素。在财务因素模型中，未知因素可以通过一些可观察的代理（如Fama-French因素）得到合理的解释。在扩散指数预测中，确定的因素与几个直接可测量的经济变量密切相关，如消费财富变量，财务比率和期限利差。有了这些协变量，即使只有有限维，因子和载荷都可以识别到旋转矩阵。为了结合这些协变量的解释能力，我们提出了一种平滑或投影主成分分析（PCA）：（i）将数据回归到观察到的协变量上，（ii）采用拟合数据的主成分来估计载荷和因子。这使我们能够更准确地估计因子中已解释和未解释成分的百分比，从而评估协变量的解释力。我们表明，估计的因素和负载可以估计与改进的收敛速度相比，基准方法。改善的程度取决于信号的强度，代表协变量对因子的解释力。建议的估计是强大的可能重尾分布。我们应用该模型预测美国债券风险溢价，发现所观察到的宏观经济特征包含了很强的解释力的因素。将这些特征结合起来估计公共因子，比直接用于预测，预测的收益更大。
We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama–French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the factors and loadings are identifiable up to a rotation matrix even only with a finite dimension. To incorporate the explanatory power of these covariates, we propose a smoothed or projected principal component analysis (PCA): (i) regress the data onto the observed covariates, and (ii) take the principal components of the fitted data to estimate the loadings and factors. This allows us to more accurately estimate the percentage of both explained and unexplained components in factors and thus to assess the explanatory power of covariates. We show that both the estimated factors and loadings can be estimated with improved rates of convergence compared to the benchmark method. The degree of improvement depends on the strength of the signals, representing the explanatory power of the covariates on the factors. The proposed estimator is robust to possibly heavy-tailed distributions. We apply the model to forecast US bond risk premia, and find that the observed macroeconomic characteristics contain strong explanatory powers of the factors. The gain of forecast is more substantial when the characteristics are incorporated to estimate the common factors than directly used for forecasts.