Working Papers
This paper introduces the U.S. Monetary Policy Event-Study Database (USMPD), a novel, public, and regularly updated dataset of financial market data around Federal Open Market Committee (FOMC) policy announcements, press conferences, and minutes releases. Using the rich high-frequency data in the USMPD, we document several new empirical findings. Large monetary policy surprises have made a comeback in recent years, and post-meeting press conferences have become the most important source of policy news. Monetary policy surprises have pronounced negative effects on breakeven inflation based on Treasury yields. Risk assets, including dividend derivatives, also respond strongly and negatively to monetary policy surprises, consistent with conventional channels of monetary transmission. Press conferences have stronger effects than FOMC statements on most asset prices. Finally, the term structure evidence shows peak effects on market-based inflation and dividend expectations at horizons of several years.
We estimate the effect of monetary policy on financial vulnerabilities and the implications for risks to the economic growth outlook. We extract a small number of factors from a large dataset of financial vulnerability indicators that contain predictive information on tail risk for economic growth. We find that vulnerabilities arising from asset valuation pressures (i.e., price of risk indicators) drive short-term risks to the macroeconomic outlook, while indicators of vulnerabilities arising from non-financial and financial institution balance sheet vulnerabilities (i.e., quality of risk indicators) drive medium-run risks. We include price and quantity of risk factors in a proxy SVAR, and show that an unexpected tightening in the monetary policy stance increases vulnerabilities that are predictive of short-horizon tail risk, while reducing tail risk vulnerabilities in the medium term.
We revisit the role of long-term nominal corporate debt for the transmission of inflation shocks in the general equilibrium model of Gomes, Jermann, and Schmid (2016). We show that inaccuracies in the model solution and calibration strategy lead GJS to a model equilibrium in which nominal long-term debt is systematically mispriced. As a result, the quantitative importance of corporate leverage in the transmission of inflation shocks to real activity in their framework is 6 times larger than what arises under the rational expectations equilibrium.
We build a new measure of credit and financial market sentiment using Natural Language Processing on Twitter data. We find that the Twitter Financial Sentiment Index (TFSI) correlates highly with corporate bond spreads and other price- and survey-based measures of financial conditions. We document that overnight Twitter financial sentiment helps predict next day stock market returns. Most notably, we show that the index contains information that helps forecast changes in the U.S. monetary policy stance: a deterioration in Twitter financial sentiment the day ahead of an FOMC statement release predicts the size of restrictive monetary policy shocks. Finally, we document that sentiment worsens in response to an unexpected tightening of monetary policy.
Publications
Solo-authored
I use micro data to quantify key features of US firm financing. In particular, I establish that a substantial 35 percent of firms' investment is funded using financial markets. I then construct a dynamic equilibrium model that matches these features and fit the model to business cycle data using Bayesian methods. In the model, financial intermediaries enable trades of financial assets, directing funds toward investment opportunities, and charge an intermediation spread to cover their costs. According to the model estimation, exogenous shocks to the intermediation spread explain 25 percent of GDP and 30 percent of investment volatility.
We propose a no-arbitrage model of the nominal and real term structures that accommodates the different persistence and volatility of distinct inflation components. Core, food, and energy inflation combine into a single total inflation measure that ties nominal and real risk-free bond prices together. The model successfully extracts market participants' expectations of future inflation from nominal yields and inflation data. Estimation uncovers a factor structure common to core inflation and interest rates and downplays the pass-through effect of short-lived food and energy shocks on inflation and interest rates. Model forecasts systematically outperform survey forecasts and other benchmarks.
We study optimal interest-rate policy in a New Keynesian model in which the economy can experience financial crises and the probability of a crisis depends on credit conditions. The optimal adjustment to interest rates in response to credit conditions is (very) small in the model calibrated to match the historical relationship between credit conditions, output, inflation, and likelihood of financial crises. Given the imprecise estimates of key parameters, we also study optimal policy under parameter uncertainty. We find that Bayesian and robust central banks will respond more aggressively to financial instability when the probability and severity of financial crises are uncertain.
What is the role of arbitrage trading in the U.S. Treasury market? In this article, the authors discuss the pricing of risk-free Treasury securities via no-arbitrage arguments and illustrate how this approach works in models of the term structure of interest rates. The article ends with an evaluation of market frictions (for example, transaction costs, leverage constraints, and the limited availability of arbitrage capital) in the government debt market and their implications for bond pricing using no-arbitrage term structure models.
Policy Analysis
Older Work