We decompose market return variance into a component that covaries with the stochastic discount factor (SDF) and one that does not. The SDF-orthogonal component - conditional excess volatility - contributes to variance but cannot earn a risk premium. We show that the ratio of the market risk premium to total market variance reveals the share of market variance orthogonal to the SDF. Using three distinct measures of this ratio across twenty equity markets, we find that it does not increase during recessions and often declines. Because standard models uniformly predict countercyclical risk pricing, this finding requires the excess volatility share to increase substantially in bad times. Our empirical results imply that excess volatility constitutes at least half of the total market volatility in recessions. We show that this mechanism provides a unified explanation for (i) why optimal portfolio weights do not increase in downturns despite rising Sharpe ratios, (ii) why the term structure of Sharpe ratios on dividend claims is downward-sloping, and (iii) why option portfolios with fixed risk quantities earn lower returns at longer horizons.

We study how aggregate financial conditions shape firm insurance and, through it, labor income risk. In a directed search model with dynamic wage contracts and two-sided limited commitment, firm insurance against idiosyncratic shocks erodes when risk premia rise. Using U.S. administrative data, we document new evidence supporting the model: pass-through of firm shocks to earnings rises in bad times, especially for lower-paid workers near the separation margin. The model reproduces many untargeted time-series and cross-sectional features of earnings risk and implies substantial welfare costs of idiosyncratic risk, high private discount rates on human capital, and large gains from recession-contingent transfers.

The U.S. stock market return during the first month of a quarter positively predicts the

second month’s return, which in turn negatively predicts the first month’s return of the

next quarter. This pattern arises because investors fail to fully recognize that earnings

announced in the second month of a quarter are inherently similar to those announced

in the first month, leading them to overreact to predictably repetitive earnings news.

A model formalizing this form of correlation neglect yields additional predictions for

survey data and for both the time-series and cross-section of returns, all of which are

borne out in the data. These results provide evidence of correlation neglect even among

sophisticated, financially incentivized decision-makers, underscoring its importance as

a behavioral phenomenon.