In this thesis, we tackle the question of how newly available public information is absorbed in the FX market. The existing literature uses a standardized news transformation on macroeconomic data before using it in time-series models, due to a link between the transformation and the rational expectations hypothesis. Our results challenge a de facto approach by highlighting that the choice of the news transformation has a significant effect on the results. In addition, we propose several methodological improvements to the popular time-series approach. However, combining low frequency macroeconomic indicators and high-frequency FX processes in time-series models creates an ill-structured problem. To shed new light on the popular existing methodology, we propose an innovative way of restructuring the problem so that less restrictive methods - such as scaling laws, dominance testing and probability metrics - can be applied. Our results show weak evidence for a widely reported observation that new information causes elevated levels of volatility in FX markets, and in fact the reverse is observed in some cases. Further investigation reveals that the only significant factor driving FX news shocks is an anticipation effect of the news release. Once we account for the anticipation effect, we observe that most releases have positive influence irrespective of the sign of the data indicator released
