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The Fama-French (FF) five-factor model is cast into a dynamic setting to capture the impact of illiquidity over the phases of the business cycle on the returns of the passive FF twelve sector portfolios. We use two dynamic approaches, Kalman filtering and a recursive/rolling robust instrumental variables (IV) algorithm cast into a GMM framework, to determine time-varying alpha and beta estimates. Our principal result is that the Kalman filter approach supports the hypothesis that illiquidity is an important risk factor in a dynamic context. However, the only factor found to matter in the dynamic GMM approach is the market risk premium. Nevertheless, illiquidity may be prescient with respect to financial crises. Keywords: Illiquidity, Fama-French five-factor model, Kalman filter, Robust IV algorithm, JEL Classification: C58, G12

We investigate the new Fama-French (FF, 2015, 2016) five factors augmented with a well-known illiquidity measure (Pastor and Stambaugh, 2003), using an innovative GMM robust instrumental variables estimator casted in a panel data framework. When using OLS, the augmented FF model seems to have explanatory power regarding the FF 12-sector returns. However, our panel data framework suggests that the only consistently significant factor is the market risk factor. Nevertheless, depending on the technique we use, we find that measurement errors may be the cause of this result, thus providing some empirical evidence in support of the new FF five-factor approach. As robustness checks, we also experiment with other liquidity measures – like the Amihud (2002) ratio and the term-spread – and bond-oriented factors. Across our 12 portfolios, the results are largely unchanged. We also apply our extended model to managed portfolios – i.e., hedge fund portfolios. The returns of hedge fund strategies seem more responsive to the augmented FF five-factor model that includes illiquidity measures, especially when accounting for the subprime crisis. There is also evidence that the new FF factors embed illiquidity.

The capital asset pricing model (CAPM), Fama-French (FF), and Pastor-Stambaugh (PS) factor models are examined using a new dynamic rolling regression version of the generalized method of moments (GMM) method. This rolling regression framework not only allows us to investigate phases of the business cycle, but also permits regression estimates to vary through time due to changes in the development and efficiency of the sectors. The principal reasons for using the dynamic GMM with robust instruments is that some of these factors are measured with errors and the disturbances may be non-spherical. The CAPM appears as the most parsimonious model to explain the FF sector returns. Furthermore, the rolling GMM approach is clearly more sensitive to dynamic financial episodes than the ordinary least squares approach. In particular, liquidity has some anticipatory power, as it is able to forecast the 2007–2009 crises with heightened volatility starting in late 2005.

Fama and French (FF, 2015) propose a new five-factor asset pricing model that adds profitability and investment patterns to the market, size and value variables used in FF (1992). Our purpose is to investigate this new model using an improved generalized method of moments (GMM)-based robust instrumental variables technique in a fixed-effects panel data framework. To test for measurement errors, we use a modified Hausman artificial regression. We also examine an augmented FF six-factor model that includes the Pastor–Stambaugh (PS, 2003) liquidity factor. Using the FF dataset, our GMM-based panel data approach leads us to conclude that the only consistently significant factor is the market factor.

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.