Three essays in empirical asset pricing

The main theme of this thesis is the rationalization of new stylized facts involving both asset returns and relevant macroeconomic variables. Asset pricing models assume that risks that affect investment opportunities are related to one or several macroeconomic factors, and that these risks are compensated by appropriate returns. These models then aim at determining the risk factors that investors care the most about. Interesting asset pricing properties of consumption volatility have been put forward in earlier studies, but they were mainly related to the time series dimension of asset returns. A major contribution of our research will be to characterize and measure its impact in the cross-sectional dimension.;Further, we shed light on numerical, analytical and statistical problems that affect some conclusions of existing asset pricing models. Consumption-based equilibrium asset pricing models have regained some momentum with new insights about the connections between stock market volatility and returns, the pricing of long-run claims, or return predictability. Links are established between risk premiums and different types of preferences, where separation between the elasticity of intertemporal substitution and risk aversion, and habit formation take center stage. Often, the solution of these models necessitates an approximation and quantities of interest are computed through simulations.;The second chapter proposes a model that delivers closed-form formulas for many of the statistics usually computed to assess the ability of the models to reproduce stylized facts. The proposed model is flexible enough to capture the various dynamics for consumption and dividends as well as the different types of preferences that have been assumed in consumption-based asset pricing models. It then offers a common setting to re-evaluate various methods of resolution and usual approximations in asset pricing general equilibrium models. The availability of closed-form formulas enhances our understanding of the economic mechanisms behind empirical results and of the limits of validity for the usual approximations.;Recent developments in asset return modeling have shown evidence for time-variation in conditional higher moments, especially skewness and leverage effects. Finally, the third chapter develops a discrete time affine multifactor latent variable model of asset returns which allows for both stochastic volatility and stochastic skewness (SVS model). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows the distribution of current daily returns conditional on current volatility to be asymmetric. In our model, time-varying conditional skewness is driven by the conditional leverage effect and the asymmetry of the distribution of current returns conditional on current volatility.;The first chapter documents empirical facts showing the existence of a strong relationship between macroeconomic uncertainty and stock returns. It provides and supports the evidence that long-term investors care not only about variation between future and present consumption level (long-run consumption level risk), but also and perhaps mostly about variation between future and present macroeconomic uncertainty (long-run consumption volatility risk). We show that differences in risk premia across stocks are also due to the heterogeneity in their exposure to changes in consumption volatility. Empirical facts documented in this chapter suggest that consumption volatility risk is highly correlated to risk premium for various investment horizons, more than consumption level risk for long-period investments and less for short-period investment in stocks. Moreover, long-run volatility risk is priced even in the presence of long-run consumption risk. This study is theoretically motivated by a reduced-form affine general equilibrium model with stochastic volatility. A well-conducted calibration of such a model would rationalize theses empirical findings.;We derive analytical formulas for various moment conditions that we use for GMM inference. Applying our approach to several equity and index daily returns, we show that the conditional distribution of current daily returns, conditional on current volatility, is positively skewed and helps to match sample return skewness as well as negative cross-correlations between returns and squared returns. The conditional leverage effect is significant and negative. The conditional skewness is positive, implying that the asymmetry of the distribution of current returns conditional on current volatility dominates the leverage effect in determining the conditional skewness.;Keywords. Consumption Volatility, Volatility Risk, Cross-Section of Returns, Equilibrium Asset Pricing, Equity Premium, Risk-free Rate Puzzle, Predictability of Returns, Affine Models, Stochastic Volatility, Stochastic Skewness, Leverage Effect, GMM.