Research

Abstract: We use intraday price and volume data from the cross section of stocks in the S&P500 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 follow the volume-volatility elasticity framework to interpret our estimation. Following the literature, we consider a set of stock characteristics that might contribute to investor disagreement: idiosyncratic volatility, market size, value, and percent of institutional ownership. Our findings suggest that investors do disagree whenever there is more uncertainty about future payoffs, in which case idiosyncratic volatility has the greatest explanatory power. Furthermore, the different stock characteristics explain, to a large extent, the deviation from the case of no disagreement. Finally, 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.

Abstract: 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 diferences in local averages, followed by a minimum-distance type estimation of 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 in fill asymptotic setting, is highly non-standard and generally not mixed normal. We establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction, and further justify its practical use through a series of Monte Carlo simulation experiments. We apply the new methods to study the relationship between trading intensity and spot volatility in the U.S. equity market at the time of important macroeconomic news announcement, as well as the relationship between these jumps and announcement surprises.