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 Pástor–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.

Nous investiguons les cinq nouveaux facteurs de Fama et French (2015, 2016) rehaussés d’une mesure de liquidité bien connue (Pástor and Stambaugh, 2003) à l’aide d’une version du GMM qui recourt à des instruments robustes, cela dans le cadre d’une analyse en panel. Lorsque nous recourons à l’estimateur OLS, notre version du modèle de Fama-French semble avoir un pouvoir explicatif en regard des rendements d’un portefeuille à douze secteurs. Cependant, notre étude en panel suggère que le seul facteur significatif est la prime de risque du marché. Dépendamment de la technique utilisée, nous trouvons que les erreurs de mesure peuvent être à la source de ce résultat, ce qui tend à appuyer le modèle élargi de Fama-French. Comme test de robustesse, nous expérimentons également avec d’autres mesures de liquidité, telles que le ratio d’Amihud (2002) et la prime à terme, ainsi qu’avec des facteurs reliés aux marchés obligataires. Les résultats sont plutôt inchangés pour nos 12 portefeuilles. Nous testons également notre modèle élargi sur des portefeuilles gérés – i.e., des fonds de couverture (hedge funds). Ces portefeuilles semblent mieux adaptés au modèle élargi de Fama-French qui inclut des mesures de liquidités, et plus spécialement lorsque l’on introduit la crise des subprimes . Les nouveaux facteurs de Fama-French semblent également être très sensibles à nos mesures d’illiquidité.

Fama and French (FF, 2015) propose a five-factor asset pricing model that captures size, value, profitability and investment patterns. The primary purpose here is to further investigate this new model using an improved GMM-based robust instrumental variables technique. A further purpose is to explore the relationship among the FF factors and the Pástor–Stambaugh (PS, 2003) liquidity factor. We conclude that except for the market factor, all of the factors including liquidity are not significant at even the 5% level using our GMM approach for almost all of the FF 12 sectors.

The subprime crisis was quite damaging for hedge funds. Using the local projection method (Jordà 2004, 2005, 2009), we forecast the dynamic responses of the betas of hedge fund strategies to macroeconomic and financial shocks—especially volatility and illiquidity shocks—over the subprime crisis in order to investigate their market timing activities. In a robustness check, using TVAR (Balke 2000), we simulate the reaction of hedge fund strategies’ betas in extreme scenarios allowing moderate and strong adverse shocks. Our results show that the behavior of hedge fund strategies regarding the monitoring of systematic risk is highly nonlinear in extreme scenarios—especially during the subprime crisis. We find that countercyclical strategies have an investment technology which differs from procyclical ones. During crises, the former seek to capture non-traditional risk premia by deliberately increasing their systematic risk while the later focus more on minimizing risk. Our results suggest that the hedge fund strategies’ betas respond more to illiquidity uncertainty than to illiquidity risk during crises. We find that illiquidity and VIX shocks are the major drivers of systemic risk in the hedge fund industry.