The benefits of using large high frequency financial datasets for empirical analyses: Two applied cases

Abstract

How do markets evaluate monetary policy announcements and how large are the shocks they convey? These are central questions for policy makers if they are interested in evaluating their decisions and quantitatively assess the outcomes of different and possibly alternative policies. As we know, if markets were completely efficient and monetary policy was perfectly communicated by central banks, market agents should have already priced in the decision of the monetary authority at the time of the announcement. On the contrary, if the central banks are able to surprise the market, they might be able to generate real effects after their policies. We present a methodology to identify monetary policy shocks using high frequency financial data. When the precise moment of a shock is known, high frequency data allow us to pinpoint the exact moment of the event and, therefore, to correctly identify the reaction of market participants.