This paper defines risk-on risk-off (RORO), an elusive terminology in pervasive use, as the variation in global investor risk aversion. Our high-frequency RORO index captures time-varying investor risk appetite across multiple dimensions: advanced economy credit risk, equity market volatility,...

Over the last two decades, the unprecedented increase in non-bank financial intermediation, particularly open-end mutual funds and ETFs, accounts for nearly half of the external financing flows to emerging markets exceeding cross-border lending by global banks. Evidence suggests that investment...

We investigate the determinants of emerging markets performance during five U.S. Federal Reserve monetary tightening and easing cycles during 2004-2023. We study how macroeconomic and institutional conditions of an Emerging Market (EM) at the beginning of a cycle explain EM resilience during...

We investigate the theoretical possibility and empirical regularity of two troublesome anomalies that frequently arise when cross-price elasticities are estimated for a set of brands expected to be substitutes. These anomalies are the occurrence of: (a) negatively signed cross-elasticities; and...

The historical returns on equity index options are well known to be strikingly negative. That is typically explained either by investors having convex marginal utility over stock returns (e.g. crash/variance aversion) or by intermediaries demanding a premium for hedging risk. This paper examines...

We document that the convenience yield of U.S. Treasuries exhibits properties that are consistent with a hedging perspective of safe assets. The convenience yield tends to be low when the covariance of Treasury returns with the aggregate stock market returns is high. A decomposition of the...

We survey the growing literature emphasizing the role that supply-and-demand forces play in shaping the term structure of interest rates. Our starting point is the Vayanos and Vila (2009, 2021) model of the term structure of default-free bond yields, which we present in both discrete and...

We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

This paper describes a process for automatically generating academic finance papers using large language models (LLMs). It demonstrates the process' efficacy by producing hundreds of complete papers on stock return predictability, a topic particularly well-suited for our illustration. We first...

Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics dataset. We achieve state-of-the-art performance while maintaining...