Applications of High-Frequency Event Studies: Policy Evaluation and Financial Analysis

Abstract

High-frequency event studies exploit narrow windows around news releases to identify causal effects when randomized experiments are infeasible. Using this approach alongside panel regressions, local projections, and difference-in-differences designs, this dissertation studies key questions in monetary economics and climate finance. Chapter 2 shows that unexpected U.S. monetary policy tightenings affect growth stocks more than value stocks because growth firms’ cash flows are concentrated further in the future and are therefore more sensitive to discount-rate changes; an asset pricing model reconciles this mechanism with the value premium. Chapter 3 examines euro area firms and finds that investment responds most strongly to interest rate cuts when firms have strong fundamentals but face tight external financing. These results underscore that monetary policy effectiveness depends critically on underlying economic fundamentals beyond central banks’ direct control. The second theme of the dissertation focuses on climate economics. Chapter 4 shows that legislative milestones related to the Inflation Reduction Act increased valuations of green firms while reducing those of brown firms, without causing market disruptions or widespread stranded assets. Chapter 5 finds that euro area monetary tightening leads to larger valuation losses for brown firms than for green firms, even after controlling for firm characteristics. Chapter 6 uses GPT-4 to identify corporate green pledges in news articles and shows that these announcements raise stock prices and are followed by reductions in emissions. Overall, the dissertation demonstrates that high-frequency event studies are a powerful tool for uncovering heterogeneous and causal policy effects across firms.