Assistant Professor of Finance
Tsinghua University, School of Economics and Management, China
Ph.D. in Economics, UCLA, 2014-2020
Corporate Bond Liquidity During the COVID-19 Crisis (with Mahyar Kargar, Benjamin Lester, David Lindsay, Pierre-Olivier Weill, and Diego Zuniga), Review of Financial Studies (2021), 34(11), 5352–5401
A non-technical summary by Tom Petruno from the UCLA-Anderson Review
We study liquidity conditions in the corporate bond market since the onset of the COVID-19 pandemic. We find that in mid-March 2020, as selling pressure surged, dealers were wary of accumulating inventory on their balance sheets, perhaps out of concern for violating regulatory requirements. As a result, the cost to investors of trading immediately with a dealer surged. A portion of transactions migrated to a slower, less costly process wherein dealers arranged for trades directly between customers without using their own balance sheet space. Interventions by the Federal Reserve appear to have relaxed balance sheet constraints: soon after they were announced, dealers began absorbing inventory, bid-ask spreads declined, and market liquidity started to improve. Interestingly, liquidity conditions improved for bonds that were eligible for the Fed’s lending/purchase programs and for bonds that were ineligible. Hence, by allowing dealers to unload certain assets from their balance sheet, the Fed’s interventions may have helped dealers to better intermediate a wide variety of assets, including those not directly targeted.
Are dealers' search efforts endogeneous in decentralized markets? How do dealers' search efforts affect market efficiency? We propose a model with dealers choosing idiosyncratic search intensities, and estimate the model using transaction data on U.S. corporate bonds. We find that:  with dealers ranked by their private valuations for a bond, the middle-type dealer chooses the highest search intensity, and she reallocates bond positions from lower-type dealers to higher-type dealers;  the estimated model predicts that the search costs and bond misallocation in current OTC markets generate 13.7% welfare loss relative to a counterfactual frictionless market.
This paper analyzes OTC market participants' endogeneous search intensity in competitive equilibrium and social optimal cases. We develop a random search-and-match model where agents (market participants) are allowed to choose and adjust their search intensities based on two idiosyncratic states: asset position and liquidity need. We find that:  in competitive equilibria with different market parameters, agents can switch between the core and periphery on the trading network.  it is the social optimal case that there is no intermediation, in the sense that no agent searches at positive speeds on both the buy and sell sides of the market. In competitive equilibrium, there always exist some agents over-searching and some other agents under-searching. We also discuss related policy implications.
Systemic search friction is an important liquidity factor which drives all corporate bonds' yield spread changes. In cross section, bonds have different levels of this yield spread loading. To explain this cross-sectional heterogeneity, we propose a measure of bond-level misallocation among traders, which is defined as the covariance of traders' private valuation and inventory position for each bond. Using transaction-level data, we find that: bonds with a higher level of misallocation have a lower absolute value of yield spread loading on systemic search friction. This relationship is specific to the decentralized market structure, where transactions rely on traders’ searching activity.