2018
DOI: 10.3917/fina.393.0045
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Testing the new Fama and French factors with illiquidity: A panel data investigation

Abstract: 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 significati… Show more

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Cited by 21 publications
(17 citation statements)
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“…On the other hand [ 34 ], use a distance instrument-based algorithm into a GMM framework, to study the impact of illiquidity on the returns of twelve sector portfolios. Consistently with [ 32 , 33 ], the authors conclude that the only relevant factor according to their dynamic GMM approach is the market risk premium.…”
Section: Data and Resultssupporting
confidence: 68%
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“…On the other hand [ 34 ], use a distance instrument-based algorithm into a GMM framework, to study the impact of illiquidity on the returns of twelve sector portfolios. Consistently with [ 32 , 33 ], the authors conclude that the only relevant factor according to their dynamic GMM approach is the market risk premium.…”
Section: Data and Resultssupporting
confidence: 68%
“…247–251) explains that, under certain conditions, the Fama-MacBeth procedure is numerically equivalent to a pooled regression, and emphasizes the benefits of mapping this approach into the generalized method of moments (GMM), developed by [ 65 ]. Notably [ 66 ], introduces a GMM estimator that relies on distance instruments to estimate panel data regression models containing errors in variables [ 32 , 33 ]. use this approach to test the Fama-French five-factor model augmented with illiquidity measures, concluding that, in general, the most significant factor is RMRF, with measurement errors largely determining this result.…”
Section: Data and Resultsmentioning
confidence: 99%
“…For this sector, an increase in IML leads to a decrease in its stock return. Note that this reaction may be due to an expectation effect, an increase in illiquidity resulting in a further expected deterioration of this market dimension, giving rise to a drop in returns (see [54] and references therein). Since the return of the Health sector does not incorporate an illiquidity premium, it does not deleverage after a rise in the term spread.…”
Section: Resultsmentioning
confidence: 99%
“…Our contribution is therefore to further develop the methodology discussed in [41] and in the context of our time-varying model of risk exposures. The methodology developed in [41,54] is static. The algorithm discussed in these previous works is therefore extended to the time-varying application developed in this article.…”
Section: Robust IV Estimation Algorithm Of the Augmented Fama-french mentioning
confidence: 99%
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