UC San Diego

This dissertation addresses several questions in financial economics. A common thread is the study of conditional distributions and higher moments. The first chapter proposes state-dependent, idiosyncratic tail risk as a key driver of asset pricing dynamics. In standard models, the only sources of priced macroeconomic risk govern time- variation in aggregate consumption and/or preferences. When markets are incomplete, agents care not just about the level of consumption, but also its redistribution across agents. Administrative earnings data suggest that the conditional tails of the cross-sectional distribution of labor income growth rates are highly cyclical; its left and right tails become fatter and thinner, respectively, in recessions. These features are consistent with a model in which households are exposed to rare, very large shocks and the probability of these shocks varies over time. My paper measures, prices, and demonstrates the quantitative importance of labor market event risk. The second chapter studies investor redemption behavior from money market mutual funds in September 2008. These funds are a popular alternative to bank accounts for cash investments, particularly for large corporations and institutional investors. Like banks, MMF investments have a liquidity mismatch between assets and liabilities that can create the potential for a run. There is an active debate about the mechanisms generating run-like behavior, particularly about the importance of strategic complementarities-- investors' self-fulfilling beliefs about other investors' actions (i.e. "panic"). Data from the money market provide empirical evidence of the quantitative importance of complementarities in the data. Another area of asset pricing where time-varying distributions becomes important is option pricing. The third chapter studies the relationship between the option-implied distribution of market returns and estimates of the distribution obtained from time series methods. The ratio of the two densities, the pricing kernel, provides "model-free" insights about preferences. Standard models predict that the pricing kernel should be a monotonically decreasing function of the market return, but many estimates violate this property. We develop a formal, nonparametric test of pricing kernel monotonicity, provide general conditions under which the test is applicable, then perform the test using S&P 500 options data. We frequently reject the hypothesis of monotonicity