I'm a financial economist working on empirical asset pricing and financial intermediation, often using text as data. Here you can learn a bit more about my work.
This figure is from a recent paper titled Fundamentals of Perpetual Futures. Perpetual futures—swap contracts that never expire—are by far the most popular derivative traded in cryptocurrency markets, with more than $100 billion traded daily. But unlike fixed-maturity futures, perpetuals are not guaranteed to converge to the spot price of their underlying asset at any time, and familiar no-arbitrage prices for perpetuals are not available, as the contracts have no expiry date to enforce arbitrage. Here, using a weaker notion of random-maturity arbitrage, we derive no-arbitrage prices for perpetual futures in frictionless markets, and no-arbitrage bounds for markets with trading costs. These no-arbitrage prices provide a useful benchmark for perpetual futures and simultaneously prescribe a strategy to exploit divergence from these fundamental values. Empirically, we find that deviations of crypto perpetual futures from no-arbitrage prices are considerably larger than those documented in traditional currency markets.
This figure is from a recent paper titled The Value of Data to Fixed Income Investors. Using a structural model, we estimate the value of data to fixed income investors and study its main drivers. In the model, data is more valuable for bonds that are volatile and for which price-insensitive liquidity trades are more likely. Empirically, we find that the value of data on corporate bonds increases with yield, time-to-maturity, size, callability, liquidity, and uncertainty during normal times. However, these cross-sectional differences vanish as the value of data falls during financial crises. Using a regression discontinuity based on maturity, we provide causal evidence that investor composition affects the value of data.