We document strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982. Return predictability declined significantly during the Great Moderation in the post-1982 sample. Our empirical finding is robust to out-of-sample "real time" forecasts in terms...

This paper provides evidence of the impact of hedge funds on asset markets. We construct a simple measure of the aggregate illiquidity of hedge fund portfolios, based on the cross-sectional average first order autocorrelation coefficient of hedge fund returns, and show that it has strong and...

We show that realized volatility, especially the realized volatility of financial sector stock returns, has strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time....

We introduce a portfolio friction in a two-country DSGE model where investors face a constant probability to make new portfolio decisions. The friction leads to a more gradual portfolio adjustment to shocks and a weaker portfolio response to changes in expected excess returns. We apply the model...

Bitcoin has been described as a decentralized, partially anonymous, virtual currency, not backed by any government or other legal entity. Bitcoins are highly liquid, have low transaction costs, and are very volatile. This paper will look at the behavior of the value of Bitcoins, forecast the...

We document a significant shift in the comovement of asset returns and macroeconomic volatility during the Great Moderation. Strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982 was followed by a significant predictability decline during the...

We document strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982, and a significant decline in this predictability during the Great Moderation. These findings are robust to alternative empirical specifications and out-of-sample tests. We...

We analyze short-term futures oil pricing over the 2003-2016 time-period in order to analyze the bubble-like dynamics, which characterizes the 2007-2009 years according to a large body of recent literature. Our investigation, based on a flexible three-agent model (hedgers, fundamentalist...

We use machine learning techniques to conduct out-of-sample predictions of the underpricing of U.S. initial public offerings (IPOs) from 1990 to 2019. Using predicted underpricing based on ex ante information to sort the IPOs into 10 groups, we find that the underpricing averages for the top and...