The Impact of Macroeconomic News on Quote Adjustments, Noise, and Informational Volatility

Abstract

We decompose bid and ask quote log returns into three linear latent components: a common factor reflecting the informative efficient return and two noisy components capturing market side specific frictions, like liquidity-induced quote revisions. The latent return components are assumed to follow a conditionally heteroscedastic VAR(1) model where the conditional means and variances of the individual components are augmented by appropriate regressors capturing the impact of news arrivals. The model is applied to study the impact of the announcement of U.S. employment headline figures on high-frequency returns of the German Bund future traded at EUREX. We show that (non-anticipated) news induces sharp and instantaneous shifts in ask and bid quotes depending on the magnitude and the sign of the news. Moreover, clear evidence for strong reactions in the corresponding conditional variance components is found. In general, news increases the relative share of the efficient return volatility in the conditional quote return variances. This is particularly true when the new information is perceived to be imprecise. On the other hand, if the announced figure is of high precision, conditional variances are more dominated by the noise volatility.