Telfer School of Management, University of Ottawa, Ottawa, ON, Canada.
Affiliate Research Fellow, IPAG Business School, Paris, France.
PLoS One. 2019 Sep 18;14(9):e0221599. doi: 10.1371/journal.pone.0221599. eCollection 2019.
We investigate conditional specifications of the five-factor Fama-French (FF) model, augmented with traditional illiquidity measures. The motivation for this time-varying methodology is that the traditional static approach of the FF model may be misspecified, especially for the endogenous illiquidity measures. We focus on the time-varying nature of the Jensen performance measure α and the market systematic risk sensitivity β, as these parameters are essentially universal in asset pricing models. To tackle endogeneity and other specification errors, we rely on our robust instrumental variables (RIV) algorithm implemented via a GMM approach. In this dynamic or time-varying conditional context, we generally find that the most significant factor is the market one, but illiquidity may matter depending on which states or estimation methods we consider. In particular, sectors whose returns embed a market illiquidity premium are more exposed to a binding funding constraint in times of crisis, which leads to deleveraging and a resulting decrease in systematic risk.
我们研究了 Fama-French（FF）五因子模型的条件规格，增加了传统的非流动性指标。这种时变方法的动机是，FF 模型的传统静态方法可能存在误设定，特别是对于内生非流动性指标。我们专注于 Jensen 业绩衡量指标 α 和市场系统风险敏感度 β 的时变性质，因为这些参数在资产定价模型中本质上是普遍的。为了解决内生性和其他规格错误，我们依赖于我们通过 GMM 方法实现的稳健工具变量（RIV）算法。在这种动态或时变的条件背景下，我们通常发现最显著的因素是市场因素，但非流动性可能很重要，具体取决于我们考虑的状态或估计方法。特别是，在危机时期，回报中嵌入市场非流动性溢价的部门更容易受到约束性融资约束的影响，这导致去杠杆化和系统风险的相应下降。
