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Session Overview
Session
MM-2: Market Microstructure: Liquidity and Volume
Time:
Saturday, 24/Aug/2019:
9:00 - 10:30

Session Chair: Anna Obizhaeva, New Economic School
Location: D -105

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Presentations

Market Microstructure Invariance: A Dynamic Equilibrium Model

Anna Obizhaeva, Albert Kyle

New Economic School, Russian Federation

Discussant: Dion Bongaerts (Erasmus University Rotterdam)

Invariance relationships are derived in a dynamic, infinite-horizon, equilibrium model of adverse selection with risk-neutral informed traders, noise traders, risk-neutral market makers, and endogenous information production. Scaling laws for bet size and transaction costs require the assumption that the effort required to generate one bet does not vary across securities and time. Scaling laws for pricing accuracy and market resiliency require the additional assumption that private information has the same signal-to-noise ratio across markets. Prices follow a martingale with endogenously derived stochastic volatility. Returns volatility, pricing accuracy, market depth, and market resiliency are closely related to one another. The model solution depends on two state variables: stock price and hard-to-observe pricing accuracy. Invariance makes predictions operational by expressing them in terms of log-linear functions of easily observable variables such as price, volume, and volatility.

efa2019-MM-2-1538-Market Microstructure Invariance.pdf


Liquidity, Volume, and Volatility

Vincent Bogousslavsky1, Pierre Collin-Dufresne2

1Boston College; 2SFI@EPFL

Discussant: Bart Yueshen (INSEAD)

We examine the relation between liquidity, volume, and volatility using a comprehensive sample of U.S. stocks in the post-decimalization period. For large stocks, effective spread and volume are positively related in the time series even after controlling for volatility, contrary to most theoretical predictions. This relation is mostly driven by the systematic component of volume. In contrast, for small stocks the evidence matches the predictions of standard adverse selection models. We show that the volatility of order imbalances can reconcile our puzzling finding with standard intuition. Order imbalance volatility is a primary driver of spreads and makes the relation between spread, volume, and volatility close to what is predicted by recent invariance theories. We develop a theoretical model to explain our empirical findings. Finally, we provide intraday evidence that supports our daily results.

efa2019-MM-2-1663-Liquidity, Volume, and Volatility.pdf


Do ETFs Increase Liquidity?

Mehmet Saglam1, Tugkan Tuzun2, Russ Wermers3

1University of Cincinnati, United States of America; 2Federal Reserve Board; 3University of Maryland

Discussant: Anna D. Scherbina (Brandeis University)

This paper investigates the impact of the presence of exchange-traded funds (ETFs) on the liquidity of their underlying stockholdings. Using a difference-in-differences methodology for large changes in the portfolio weights of stocks in the S&P 500 and NASDAQ 100 indexes, we find that increases in ETF ownership decrease the transaction costs of stocks. Moreover, we find evidence suggesting that high ETF ownership stocks have high price resilience. However, ETFs are linked to a higher cost of liquidation during the 2011 U.S. debt-ceiling crisis, suggesting that stocks having high-ETF ownership may experience impaired liquidity during major market stress events.

efa2019-MM-2-901-Do ETFs Increase Liquidity.pdf


 
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