Haoxiang Zhu

Do Dark Pools Harm Price Discovery

Dark pools are equity trading systems that do not publicly display orders. Dark pools offer potential price improvements but do not guarantee execution. Informed traders tend to trade in the same direction, crowd on the heavy side of the market, and face a higher execution risk in the dark pool, relative to uninformed traders. Consequently, exchanges are more attractive to informed traders, and dark pools are more attractive to uninformed traders. Under certain conditions, adding a dark pool alongside an exchange concentrates price-relevant information into the exchange and improves price discovery. Improved price discovery coincides with reduced exchange liquidity.

Finding a Good Price in Opaque Over-the-Counter Markets

This article offers a dynamic model of opaque over-the-counter markets. A seller searches for an attractive price by visiting multiple buyers, one at a time. The buyers do not observe contacts, quotes, or trades elsewhere in the market. A repeat contact with a buyer reveals the sellers reduced outside options and worsens the price offered by the revisited buyer. When the asset value is uncertain and common to all buyers, a visit by the seller suggests that other buyers could have quoted unattractive prices and thus worsens the visited buyers inference regarding the asset value. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Are CDS Auctions Biased

We study the design of credit default swaps (CDS) auctions, which determine the payments by CDS sellers to CDS buyers following defaults of bonds. Using a simple model, we find that the current design of CDS auctions leads to biased prices and inefficient allocations. This is because various restrictions imposed in CDS auctions prevent certain investors from participating in the price discovery and allocation process. The imposition of a price cap or floor also gives dealers large influence on the final auction price. We propose an alternative double auction design that delivers more efficient price discovery and allocations.

Welfare and Optimal Trading Frequency in Dynamic Double Auctions

We characterize ex post equilibria in uniform-price double auctions of divisible assets. Bidders receive private signals and inventories, have interdependent and linearly decreasing marginal values, and bid with demand schedules. In a static double auction we characterize a linear ex post equilibrium, in which no bidder would deviate from his strategy even if he would observe the private information of other bidders. Moreover, under certain conditions this ex post equilibrium is unique. The ex post equilibrium aggregates dispersed private information and is robust to distributional assumptions and details of market design. In a dynamic market with a sequence of double auctions and stochastic arrivals of new signals, we characterize a dynamic ex post equilibrium, whose allocation path converges exponentially in time to the ecient level. We demonThis paper studies the welfare consequence of increasing trading speed in financial markets. We build and solve a dynamic trading model, in which traders receive private information of asset value over time and trade strategically with demand schedules in a sequence of double auctions. A stationary linear equilibrium and its efficiency properties are characterized explicitly in closed form. Infrequent trading (few double auctions per unit of time) leads to a larger market depth in each trading period, but frequent trading allows more immediate asset re-allocation after new information arrives. Under natural conditions, the socially optimal trading frequency coincides with information arrival frequency for scheduled information releases, but can (far) exceed information arrival frequency for stochastic information arrivals. If traders have heterogeneous trading speeds, fast traders prefer the highest feasible trading frequency, whereas slow traders tend to prefer a strictly lower frequency.

What is the Optimal Trading Frequency in Financial Markets

This article studies the impact of increasing trading frequency in financial markets on allocative efficiency. We build and solve a dynamic model of sequential double auctions in which traders trade strategically with demand schedules. Trading needs are generated by time-varying private information about the asset value and private values for owning the asset, as well as quadratic inventory costs. We characterize a linear equilibrium with stationary strategies and its efficiency properties in closed form. Frequent trading (more double auctions per unit of time) allows more immediate asset reallocation after new information arrives, at the cost of a lower volume of beneficial trades in each double auction. Under stated conditions, the trading frequency that maximizes allocative efficiency coincides with the information arrival frequency for scheduled information releases, but can far exceed the information arrival frequency if new information arrives stochastically. A simple calibration of the model suggests that a moderate market slowdown to the level of seconds or minutes per double auction can improve allocative efficiency for assets with relatively narrow investor participation and relatively infrequent news, such as small- and micro-cap stocks.

Dynamic Information Asymmetry, Financing, and Investment Decisions

We study the optimal timing of security issuance to finance a new project when the firms assets in place have unobservable quality. Stochastic cash flows generated by assets in place reveal information about their quality and simultaneously reduce the required outside funding. A high-quality firm optimally delays issuance unless its accumulated cash or the market belief about its quality is sufficiently high. A low-quality firm does the same and, additionally, issues if market belief and accumulated cash are sufficiently low. Under stated restrictions, the renegotiation-proof optimal security pays outside investors in full before paying anything to original shareholders.

Swap Trading after Dodd-Frank: Evidence from Index CDS

The Dodd-Frank Act mandates that certain standard OTC derivatives be traded on swap execution facilities (SEF). This paper provides a granular analysis of SEF trading mechanisms, using message-level data for May 2016 from the two largest customer-todealer SEFs in index CDS markets. Both SEFs offer various execution mechanisms that differ in how widely customers’ trading interests are exposed to dealers. A theoretical model shows that although exposing the order to more dealers increases competition, it also causes a more severe winner’s curse. Consistent with this trade-off, the data show that customers contact fewer dealers if the trade size is larger or nonstandard. Dealers are more likely to respond to customers’ inquiries if fewer dealers are involved in competition, if the notional size is larger, or if more dealers are making markets. Finally, dealers’ quoted spreads and customers’ transaction costs increase in notional quantity and the number of dealers involved. In addition to results related to the winner’s curse, past trading relationships also affect customers’ requests and dealers’ responses. Our results contribute to the understanding of swaps markets by providing insights into the trade-offs faced by investors and dealers.

Risk and Return Trade-Off in the U.S. Treasury Market

This paper characterizes the risk-return trade-o in the U.S. Treasury market. We propose a discrete-time no-arbitrage term structure model, in which bond prices are solved in closed form and the conditional variances of bond yields are decomposed into a short-run component and a long-run component, each of which follows a GARCH-type process. Estimated using Treasury yields data from January 1962 to August 2007, our model simultaneously matches the conditional volatility dynamics and the deviation from the expectations hypothesis in the data. We nd that a higher short-run volatility component of bond yields signicantly predicts a higher future excess return, above and beyond the predictive power of the yields. The long-run volatility component does not predict bond excess returns.