I'm a PhD student in Economics at the Stanford Graduate School of Business. Using tools from industrial organization and microeconomic theory, I work on the design and analysis of markets. I'm on the job market in 2018-2019, and I'll be available for interviews at the ASSA 2019 Annual Meeting. My CV is here, and you can reach me at email@example.com.
In derivative contract markets, agents trade a variety of contracts with cash payments linked to price benchmarks. These benchmarks are set based on trade prices of some underlying asset. The volume of derivative contracts is often much larger than the volume of trade used to construct benchmarks, so these markets may be vulnerable to manipulation: contract holders may trade the underlying asset in order to move benchmarks and influence contract payoffs. While manipulation is a pervasive problem in derivative contract markets, we do not know how to tell whether a given contract market may be vulnerable to manipulation. This paper quantifies manipulation incentives in derivative contract markets. Contract markets can be much larger than underlying markets without substantially distorting price benchmarks, as long as underlying markets are sufficiently competitive. I develop methods to precisely estimate manipulation-induced benchmark distortions using commonly observed market data. I propose a simple manipulation index which can be used as a diagnostic metric, similar to the HHI, to detect potentially manipulable contract markets. I apply my results to study contract market competitiveness using the CFTC Commitments of Traders reports, to measure the manipulability of the LBMA gold price, and to propose a less manipulable design for the CBOE VIX.
A large body of work in economics studies optimally allocating assets using auctions; comparatively little work analyzes how to design use licenses for the assets that are auctioned. In this paper, we argue that license design faces a fundamental tradeoff. Long-term or perpetual licenses improve incentives for owners to invest to maintain and improve assets, but short-term licenses are better for allocative efficiency. We propose a new license, called the depreciating license, which improves on this tradeoff. Depreciating license owners periodically announce valuations at which they are willing to sell their licenses, and pay a percent of these valuations as license fees. This encourages reallocation while creating high and time-stationary investment incentives.
A Mechanism Design Approach to Identification and Estimation, with Brad Larsen, July 2018.
In many trading games, such as auctions and bargaining, agents take actions which affect the probability that they receive a good and monetary transfer payments they make or receive. In this paper, we show that agents' choices on a menu of probabilities and transfers available in equilibrium can be used to identify agents' values in many such trading games. This "empirical menu" approach can accomodate various extensions, such as certain kinds of unobserved heterogeneity and partially observed actions. We apply these results to study bargaining efficiency, competition and surplus division in used car bargaining.
Auctions with Liquidity Subsidies, November 2018.
This paper proposes liquidity subsidies for improving allocative efficiency and price discovery in multi-unit auctions. In the proposed subsidy scheme, the market administrator divides some amount of subsidy revenue between agents proportional to their marginal contribution to the slope of auction aggregate demand at the equilibrium price. These subsidies cause agents to bid more aggressively, increasing the slopes of their submitted bid curves. This decreases bid shading, increases allocative efficiency, and lowers the variance of auction prices.
Implementability, Walrasian Equilibria, and Efficient Matchings, with Piotr Dworczak, Economics Letters, 2017, 153 pp. 57–60.
Thickness in the US Housing Market, with Nadia Kotova
A Machine Learning Approach to Bargaining Game Estimation, with Brad Larsen