Asset Pricing: Market Microstructure
Paper Session
Friday, Jan. 6, 2023 10:15 AM - 12:15 PM (CST)
- Chair: Viktor Todorov, Northwestern University
Option Auctions
Abstract
All option trades must occur on exchanges, many of which offer auctions that improve prices over existing quotes. Brokers initiating auctions must be willing to trade at the existing best quote or better. For S&P500 stocks these auctions make up 23% of options volume and offer substantial price improvement of 50% of the quoted half-spread. Consistent with less informed orders being cream-skimmed, auctions have lower price impact and occur more when spreads are wider, volatility is higher, and arbitrage is more likely. While auction price improvement is large, auctions do not appear fully competitive, possibly because brokers have better knowledge of clients' informational advantages.Discrete Price, Discrete Quantity, and the Optimal Price of a Stock
Abstract
Economists commonly assume that price and quantity are continuous variables, while in reality both are discrete. As U.S. regulations mandate a one-cent minimum tick size and a 100-share minimum lot size, we predict that less volatile and more active stocks will choose higher prices to make pricing more continuous and quantity more discrete. Despite heterogeneous optimal prices, all firms achieve their optimal prices when their bid–ask spreads equal two ticks, i.e. when frictions from discrete pricing equal those from discrete lots. Empirically, our theoretical model explains 57% of cross-sectional variations in stock prices and 81% of cross-sectional variations in bid–ask spreads. The adjustment toward optimal prices rationalizes 91% of stock splits and contributes 94 bps to split announcement returns. The median U.S. stock value would increase by 106 bps and the total U.S. market capitalization would increase by $93.7 billion if all firms chose their optimal price.Market Microstructure Invariance: A Meta-Model Approach
Abstract
Theoretical predictions about liquidity are usually hard to test empirically because theyare expressed in terms of hard-to-observe quantities. We bridge the gap between theory and
practice by building a meta-model which is comprised of several simple generic equations,
likely to be shared bymost models. This yields many testable quantitative predictions in the
formof scaling laws; they are expressed in terms of the easily measurable liquidity metrics,
which are proportional to the cube root of the ratio of dollar volume to returns variance.
These predictions are consistent with existing theoretical models, if time in these models is
interpreted not as a calendar time but rather as a security-specific business time to match
volume and volatility. When mapped into theoretical models, adverse selection shows up in
pricing accuracy and resiliency. Our approach thus highlights a deep connection between
concepts of adverse selection, liquidity, and time.
Discussant(s)
Christine Parlour
,
University of California-Berkeley
Neil Pearson
,
University of Illinois-Urbana-Champaign
Torben Andersen
,
Northwestern University
Ciamac Moallemi
,
Columbia University
JEL Classifications
- G1 - Asset Markets and Pricing