Abstract
The paper examines the effect of monetary policy statement shocks on exchange rates. I use Google's Natural Language tools to measure and track changes in the sentiment of FOMC and ECB post-meeting statements. The results reveal a negative relationship between the dollar's value and FOMC statement shocks. Investors sell (buy) the dollar when the sentiment of the FOMC statement is more positive (negative) than the previous one. This negative relationship could be explained by the special status of the U.S. dollar as a safe-haven currency and the significant effect of U.S. monetary policy on other countries' macroeconomic fundamentals. The value of the euro is positively related to ECB statement shocks. The size of the exchange rate response to statement shocks is comparable to that of term structure shocks. There is no material difference between the response of exchange rates in conventional and unconventional times. Statement shocks affect the exchange rates through the information channel.
Highlights
Major central banks usually release a statement after their monetary policy meetings
Suppose we accept that international spillover of U.S monetary policy or the safe-haven status of the U.S dollar explains the negative relationship between statement shocks and dollar exchange rates
There should be a positive relationship between the sentiment of European Central Bank (ECB) statements and euro exchange rates because the international spillover effect of ECB policies to countries outside the eurozone is relatively limited
Summary
Major central banks usually release a statement after their monetary policy meetings. The statements often provide central bankers' assessment of the economy's current state and policymakers' outlook for medium to long-term economic fundamentals and monetary policy. The primary challenge in extracting information from the statements is that objective analysis of the information embedded in the statements is not an easy task, and various individuals might interpret the same sentence differently. This paper overcomes this challenge by using an objective machine learning tool to track changes in the statements. The results of this study help market participants and policymakers better understand the link between the sentiment of monetary policy statements and exchange rates
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