Essays in empirical asset pricing
This thesis has three chapters in which I develop tools for comparisons and dynamic analysis of linear asset pricing models.  In the first chapter, I introduce the notion of dynamically useless factors: factors that may be useless (uncorrelated with the assets returns) at some periods of time, while relevant at other periods of time. This notion bridges the literature on classical empirical asset pricing and the literature on useless factors, where both assume that the relevance of a factor remains constant through time. In this new framework, I propose a modified Fama-Macbeth procedure to estimate the time-varying risk premia from conditional linear asset pricing models. At each date, my estimator consistently estimates the conditional risk premium for every useful factor and is robust to the presence of the dynamically useless ones. I apply this methodology to the Fama-French five-factor model and find that, with the exception of the market, all the factors of this model are dynamically useless, although they remain useful 90 percent of the time.  In the second chapter, I infer the time-varying parameters of a potentially misspecified stochastic discount factor (SDF) model. I extend the model of Gospodinov et al. (2014) to the framework of conditional SDF models, as the coefficients and the covariances are allowed to vary over time. The proposed misspecification-robust inference is able to eliminate the negative effects of potential useless factors, while maintaining the relevance of the useful ones. Empirically, I analyze the dynamical relevance of each factor in seven common asset pricing models from 1963 to 2016. The Fama-French’s three-factor model (FF3) and five factor model (FF5) have been the overall best SDFs in the last 50 years. However, since 2000, the best SDF is CARH (FF3 + momentum factor), followed by FF5 as the second best. Apart from traded factors, the results bring a nuance on non-traded factors. We analyze the relevance, for linear pricing, of a human capital model inspired by Lettau & Ludvigson (2001) and Gospodinov et al. (2014).  The third chapter proposes a method for ranking Fama-French linear factor models according to investors’ preference for higher-order moments. I show that adding a new Fama- French factor to a prior Fama-French model systematically leads to a better model, only when the preference for higher-order moments is moderate (in absolute value). When the preference for higher-order moments is important or extreme, the four-factor model of Carhart (1997) has a better pricing ability than all the Fama-French models. An analysis of models with non-traded factors confirms the relevance, for linear pricing, of the human capital model analyzed in the second chapter. However, I show that this relevance is effective only for investors with null or very low preferences for higher-order moments.
Distance de Hansen-Jagannathan Useless factors Risk premium Stochastic discount factors Misspecified models Hansen-Jagannathan distance Discrepancy functions Higher-order moments