Essays in empirical asset pricing

In this thesis, I study asset pricing models of stock and bond returns, and therole of macroeconomic factors in explaining and forecasting their dynamics. The first chapter is devoted to the identification and measurement of risk premia in the cross-section of stocks, when some of the risk factors are only weakly related to asset returns and, as a result, spurious inference problems are likely to arise. I develop a new estimator for cross-sectional asset pricing models that, simultaneously, provides model diagnostic and parameter estimates. This novel approach removes the impact of spurious factors and restores consistency and asymptotic normality of the parameter estimates. Empirically, I identify both robust factors and those that instead suffer from severe identification problems that render the standard assessment of their pricing performance unreliable (e.g. consumption growth, human capital proxies and others). The second chapter extends the shrinkage-based estimation approach to the class of affine factor models of the term structure of interest rates, where many macroeconomic factors are known to improve the yield forecasts, while at the same time being unspanned by the cross-section of bond returns. In the last chapter (with Christian Julliard), we propose a simple macro model for the co-pricing of stocks and bonds. We show that aggregate consumption growth reacts slowly, but significantly, to bond and stock return innovations. As a consequence, slow consumption adjustment (SCA) risk, measured by the reaction of consumption growth cumulated over many quarters following a return, can explain most of the cross-sectional variation of expected bond and stock returns. Moreover, SCA shocks explain about a quarter of the time series variation of consumption growth, a large part of the time series variation of stock returns, and a significant (but small) fraction of the time series variation of bond returns, and have substantial predictive power for future consumption growth

Forest Through the Trees: Building Cross-Sections of Stock Returns

We show how to build a cross-section of asset returns, that is, a small set of basis or test assets that capture complex information contained in a given set of stock characteristics and span the Stochastic Discount Factor (SDF). We use decision trees to generalize the concept of conventional sorting and introduce a new approach to the robust recovery of the SDF, which endogenously yields optimal portfolio splits. These low-dimensional value-weighted long-only investment strategies are well diversified, easily interpretable, and reflect many characteristics at the same time. Empirically, we show that traditionally used cross-sections of portfolios and their combinations, especially deciles and long-short anomaly factors, present too low a hurdle for model evaluation and serve as the wrong building blocks for the SDF. Constructed from the same pricing signals as conventional double or triple sorts, our cross-sections have significantly higher (up to a factor of three) out-of-sample Sharpe ratios and pricing errors relative to the leading reduced-form asset pricing models