Macro Announcement Disagreement with Jump Regressions

This dissertation consists of two main essays in which it extends our knowledge on how stock market investors process the information from macro announcements. In the first essay, we extend the existing econometric theory to study the relation between jumps in multiple processes at a high-frequency. More specifically, we develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as special cases. The resulting asymptotic distribution of the estimator, derived under an infill asymptotic setting, is highly nonstandard and generally not mixed normal. In addition, we establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction. In the second essay, the new methods are applied to determine whether investors disagree when they process relevant macro-news announcements. If investors do disagree, we investigate the systematic components that drive disagreement. The high frequency data on stocks price and trade enable us to precisely isolate the news impact, and we use the volume-volatility elasticity framework to interpret our estimation. We consider a set of stock characteristics that might contribute to investor disagreement: idiosyncratic volatility, market size, value, and institutional ownership. Our findings suggest that investors do disagree whenever there is more uncertainty about future payoffs. Furthermore, the different stock characteristics explain, to a large extent, the deviation from the case of no disagreement. For last, we explore how the direction of stock misprice affects the elasticity and verify that the overall investor disagreement may not be entirely observed due to arbitrage constraints.