Lynda Khalaf

big data and econometrics seminar

Lynda Khalaf

Carleton University
Arbitrage pricing, weak beta, strong beta: identification-robust and simultaneous inference
Co-écrit avec
Marie-Claude Beaulieu, Jean-Marie Dufour
Lieu

IBD Salle 16

Îlot Bernard du Bois - Salle 16

AMU - AMSE
5-9 boulevard Maurice Bourdet
13001 Marseille

Date(s)
Mardi 17 novembre 2020| 14:00 - 15:30
Contact(s)

Michel Lubrano : michel.lubrano[at]univ-amu.fr
Pierre Michel : pierre.michel[at]univ-amu.fr

Résumé

Arbitrage Pricing Theory based factor models characterize key parameters jointly and non-linearly, which complicates identification. We propose simultaneous inference methods that preserve equilibrium relations between all model parameters including ex-post sample-dependent ones, without assuming identification. Confidence sets that invert joint tests are derived, with analytical tractable solutions. These allow one to assess whether traded and untraded factors are priced risk-drivers, and to take account of cross-sectional intercepts. A formal test for traded factor assumptions is proposed. Simulation and empirical analyses are conducted with Fama-French factors. Simulation results underscore the information content of cross-sectional intercept and traded factor restrictions. Three empirical results are noteworthy: (1) The Fama-French three factors are priced before 1970; thereafter, we find no evidence favoring any factor relative to the market. (2) Heterogeneity is not sufficient to distinguish a priced momentum from profitability or investment risk. (3) Post 1970s, factors are rejected or weakly identified, depending on intercept restrictions or test portfolios.

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