Asset Volatility in Response to Economic Indicators and Monetary Policy Communications: An Empirical Analysis

Abstract

This thesis examines how economic announcements, like those from the Federal Open Market Committee (FOMC) and Federal Funds Target Rate (FFTR), affect financial markets, looking closely at different financial products. It pays special attention to how market trends have shifted following major economic events, such as the COVID-19 pandemic in 2020. The study analyzes data from the last ten years to see how markets respond to economic updates and central bank statements, questioning how well current theories explain these reactions.
By using financial analysis and computational linguistics, the research explores three main areas: how economic news impacts market volatility, how the mood of regulatory announcements influences asset prices, and if trading algorithms can take advantage of these market changes.
The results show that markets don't always react to economic news in predictable ways, challenging traditional theories. The research also suggests that algorithmic trading could be a useful tool for dealing with market changes after economic announcements. However, attempts to use regression models to predict market reactions highlighted difficulties, particularly with omitted variables—important factors not included in the models that could affect outcomes.
This work contributes to a better understanding of how announcements related to monetary policy impact financial markets. It provides insights that could help investors and policymakers make more informed choices. Ultimately, the thesis bridges the gap between academic theories and practical financial strategies, stressing the importance of looking at both market efficiency and how real-world events influence market behavior.