UC San Diego

This dissertation studies the effect of financial asset volatility on the macroeconomy. As an important source of information, I use cross-sectional dispersion for improving volatility forecasts along with time-variation in financial assets. The first chapter, "Speculation in Commodity Futures Market, Inventories and the Price of Crude Oil", investigates the effects of financial investors' activities in commodity markets on crude oil price. While earlier researchers addressed this question based on proxies representing financial investors' activities, I develop a model of the convenience yield arising from holding crude oil inventories in spite of anticipated falling prices. Although some have argued that a breakdown of the relationship between crude oil inventories and prices following increased participation by financial investors after 2003 was evidence of an effect of speculation, I find that a correctly specified relation is stable over time. In light of this new evidence, I conclude that the contribution of financial investors' activities is weak in the crude oil market. In the second chapter, "The Usefulness of Cross-sectional Dispersion for Forecasting Aggregate Stock Price Volatility", I develop a model of stock returns where dispersion in returns across different stocks is modeled jointly with aggregate volatility. Although specifications that allow for feedback from cross-sectional dispersion to aggregate volatility have a better fit in sample, I find that such improvements are not robust for purposes of out- of-sample forecasting. Using a full cross-section of stock returns jointly, however, I find that use of cross- sectional dispersion can help improve estimates of the parameters of a GARCH process for aggregate volatility, providing better forecasts both in sample and out of sample. Given this evidence, I conclude that cross- sectional information helps predict market volatility indirectly rather than directly entering in the data- generating process. My final dissertation chapter, "Heterogeneity in the Dynamic Effects of Uncertainty on Investment", studies the effects of profit uncertainty on manufacturing firms' investment decisions. We measure aggregate profit uncertainty from quarterly industry-level sales revenues by using a Panel-ARCH model, which is a special case of the bivariate aggregate volatility model developed in the second chapter. Using estimated profit uncertainty, we find that higher profit uncertainty induces firms to lower future capital expenditure on average, yet to a different degree depending on each firm's characteristics, such as size, liability ratio, and sub-industry classification. This finding points to significant and substantial heterogeneity in the uncertainty transmission mechanism, a feature not highlighted in recent studies of uncertainty at the aggregate level