Hi, I am Sebastian Hillenbrand.

I am a 5th-year PhD student in Finance at NYU Stern and I will be on the 2021/2022 job market.

Research Areas: Asset Pricing, Macro-Finance, Financial Intermediation

Contact: shillenb@stern.nyu.edu

CV SSRN GoogleScholar

Job Market Paper:

The Fed and the Secular Decline in Interest Rates SSRN Link

MFA Best Doctoral Paper Award

The previous version was called "The Secular Decline in Long-term Yields around FOMC Meetings"

Abstract: In this paper I document a striking fact: a narrow window around Fed meetings fully captures the secular decline in U.S. Treasury yields since 1980. By contrast, yield movements outside this window are transitory and wash out over time. This is surprising because the forces behind the secular decline are thought to be independent of monetary policy. However, it is possible that the bond market learns about these forces from the Fed. Two additional facts support this interpretation: (i) long-term yields drop immediately following Fed announcements, and (ii) the Fed’s expectation about the long-run level of the federal funds rate – revealed through the dot plot – has a strong impact on long-term yields. To explain these facts, I present a dynamic term structure model in which the Fed learns from the yield curve and the market learns from Fed meetings. The model rules out alternative explanations such as business cycle information and risk premia. It further implies that the Fed possesses important information about the long-run neutral interest rate. This can explain why Fed announcements have a powerful impact on the valuations of long-lived assets like the stock market.

Working Papers:


Abstract: We introduce a heterogeneous agent model which features extrapolative beliefs and time-varying risk aversion. The model leads to an empirical framework which we estimate with stock prices, survey data and risk aversion measures. We find that extrapolative beliefs and risk aversion are important drivers of stock prices together explaining 86% of movements in the S&P500 index: extrapolative cash flow expectations explain 34%, extrapolative return expectations explain 23% and time-variation in risk aversion explains 29%. We also find that stock prices would vary by roughly 70% less if all investors were to hold rational beliefs. Our work highlights that investor heterogeneity and the use of survey data to measure their beliefs are key to understanding asset prices.

Abstract: We document three new facts about nonbank lending in the syndicated loan market. First, lending by nonbanks is about three times as cyclical as lending by banks, even after controlling for borrower demand and loan characteristics. Second, the cyclicality of nonbanks - as opposed to bank health - explains the majority of the decline in originations during both the Great Recession and the COVID-19 crisis. Third, we study the main nonbank investors in the market - CLOs and loan mutual funds. Cyclicality in flows to these institutional investors explains cyclicality in nonbank lending. We provide evidence that time-series variation in the benefit from securitization (i.e., the "CLO arbitrage") and fragility in loan mutual fund redemptions contribute to the cyclicality of nonbanks.

R&R Journal of Finance

Best Paper Award Muenster Banking Workshop

Abstract: We make use of Shared National Credit Program (SNC) data to examine syndicated loans in which the lead arranger retains no stake. We find that the lead arranger sells its entire loan share for 27 percent of term loans and 48 percent of Term B loans, typically shortly after syndication. In contrast to existing asymmetric information theories on the role of the lead share, we find that loans that are sold are less likely to become non-performing in the future. This result is robust to several different measures of loan performance and is reflected in subsequent secondary market prices. We explore syndicated loan underwriting risk as an alternative theory that may help explain this result.

Published Papers:


Journal of International Economics, 129(103418), March 2021

SCI Data Replication

Abstract: We use de-identified data from Facebook to construct a new and publicly available measure of the pairwise social connectedness between 170 countries and 332 European regions. We find that two countries trade more when they are more socially connected, especially for goods where information frictions may be large. The social connections that predict trade in specific products are those between the regions where the product is produced in the exporting country and the regions where it is used in the importing country. Once we control for social connectedness, the estimated effects of geographic distance and country borders on trade decline substantially.