It is a major challenge for asset pricing models to generate a high equity premium and a low risk-free rate while imposing realistic consumption dynamics. To address this issue, our paper proposes a novel pricing channel: we allow for consumption drops that can spark an economic crisis. This new feature generates a large equity premium even if possible consumption drops are of moderate size. In turn, our model also matches the consumption data of 42 countries along several dimensions. In particular, our approach generates a realistic number of crises that have realistic durations and involve clustering of moderate consumption drops.

It is a major challenge for asset pricing models to generate a high equity premium and a low risk-free rate while imposing realistic consumption dynamics. To address this issue, our paper proposes a novel pricing channel: we allow for consumption drops that can spark an economic crisis. This new feature generates a large equity premium even if possible consumption drops are of moderate size. In turn, our model also matches the consumption data of 42 countries along several dimensions. In particular, our approach generates a realistic number of crises that have realistic durations and involve clustering of moderate consumption drops.

Directed links in cash flow networks affect the cross-section of risk premia through three channels. In a tractable consumption-based equilibrium asset pricing model, we obtain closed-form solutions that disentangle these channels for arbitrary directed networks. First, shocks that can propagate through the economy command a higher market price of risk. Second, shock-receiving assets earn an extra premium since their valuation ratios drop upon shocks in connected assets. Third, a hedge effect pushes risk premia down: when a shock propagates through the economy, an asset that is unconnected becomes relatively more attractive and its valuation ratio increases.

When estimating misspecified linear factor models for the cross-section of expected returns using GMM, the explanatory power of these models can be spuriously high when the estimated factor means are allowed to deviate substantially from the sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. The mathematically correct global minimum of the GMM objective function can be obtained at a parameter vector that is far from the true parameters of the data-generating process. This property is not restricted to small samples, but rather holds in population. It is a feature of the GMM estimation design and applies to both strong and weak factors, as well as to all types of test assets.