nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒09‒07
thirteen papers chosen by
Thanos Verousis

  1. Trading in Crowded Markets By Stepan Gorban; Anna A. Obizhaeva; Yajun Wang
  2. Trading Liquidity and Funding Liquidity in Fixed Income Markets: Implications of Market Microstructure Invariance By Albert S. Kyle; Anna A. Obizhaeva
  3. Intraday Trading Invariance in the E-mini S&P 500 Futures Market By Torben G. Andersen; Oleg Bondarenko; Albert S. Kyle; Anna A. Obizhaeva
  4. Market Microstructure Invariance: A Dynamic Equilibrium Model By Albert S. Kyle; Anna A. Obizhaeva
  5. Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects By Peter Belcak; Peter Belcak; Jan-Peter Calliess; Stefan Zohren
  6. Industrial Organization, Order Internalization, and Invariance By Albert S. Kyle; Anna A. Obizhaeva; Yajun Wang
  7. Large Bets and Stock Market Crashes By Albert S. Kyle; Anna A. Obizhaeva
  8. Invariance of Buy-Sell Switching Points By Kyoung-hun Bae; Albert S. Kyle; Eun Jung Lee; Anna A. Obizhaeva
  9. Adverse Selection and Liquidity: From Theory to Practice By Albert S. Kyle; Anna A. Obizhaeva
  10. The Market Impact Puzzle By Albert S. Kyle; Anna A. Obizhaeva
  11. Advertising Arbitrage By Sergey Kovbasyuk; Marco Pagano
  12. Volatility Depend on Market Trades and Macro Theory By Victor Olkhov
  13. Does Vote Trading Improve Welfare? By Alessandra Casella; Antonin Macé

  1. By: Stepan Gorban (New Economic School); Anna A. Obizhaeva (New Economic School); Yajun Wang (University of Maryland)
    Abstract: We study crowded markets using a symmetric continuous-time model with strategic informed traders. We model crowdedness by assuming that traders may have incorrect beliefs about the number of smart traders in the market and the correlation among private signals, which distort their inference, trading strategies, and market prices. If traders underestimate the crowdedness, then markets are more liquid, both permanent and temporary market depths tend to be higher, traders take larger positions and trade more on short-run profit opportunities. In contrast, if traders overestimate the crowdedness, then traders believe markets to be less liquid, they are more cautious in both trading on their information and supplying liquidity to others; fears of crowded markets may also lead to "illusion of liquidity" so that the actual endogenous market depth is even lower than what traders believe it to be. Crowdedness makes markets fragile, because flash crashes, triggered whenever some traders liquidate large positions at fire-sale rates, tend to be more pronounced.
    Keywords: Asset Pricing, Market Liquidity, Market Microstructure, Crowding, Price Impact, Strategic Trading, Transaction Costs
    JEL: B41 D8 G02 G12 G14
    Date: 2020–08
  2. By: Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School)
    Abstract: This essay applies market microstructure invariance to fixed income markets. An invariance-based illiquidity measure calibrated from stock market data is extrapolated to the markets for Treasury and corporate fixed income securities. By consistently incorporating both leverage neutrality, this illiquidity measure explains both trading liquidity and funding liquidity. Invariance predicts that Treasury markets are about 55 times more liquid than markets for individual corporate bonds and operate about 3,000 times more quickly. Invariance is used to discuss repo haircuts and the flash rally of October 15, 2014.
    Keywords: market microstructure, liquidity, bid-ask spread, market impact, transaction costs, order size, invariance, fixed income, banking, systemic risk, repo markets
    Date: 2020–08
  3. By: Torben G. Andersen (Kellogg School of Management); Oleg Bondarenko (University of Illinois at Chicago); Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School)
    Abstract: The trading activity in the E-mini S&P 500 futures contract between January 2008 and September 2011 is consistent with the following high-frequency invariance relationship: The return variation per transaction is log-linearly related to trade size, with a slope coeffcient of -2. This association applies both in the time series and across a pronounced intraday pattern. The documented factor of proportionality deviates sharply from prior hypotheses relating volatility to trading intensity. High-frequency trading invariance is motivated a priori by the intuition that market microstructure invariance, introduced by Kyle and Obizhaeva (2016a) to explain bets at low frequencies, also applies to transactions over short intraday intervals. It raises the prospect of identifying periods of market stress in real time and poses intriguing challenges for market microstructure research.
    Keywords: market microstructure, invariance, high-frequency trading, liquidity, volatility, volume, time series, intraday patterns
    Date: 2020–08
  4. By: Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School)
    Abstract: We derive invariance relationships in a dynamic, infinite-horizon, equilibrium model of adverse selection with risk-neutral informed traders, noise traders, market makers, and with 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, liquidity, and market resiliency are connected by a specific proportionality relationship. 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.
    Keywords: Market microstructure, invariance, liquidity, bid-ask spread, market impact, transaction costs, market efficiency, efficient markets hypothesis, pricing accuracy, resiliency, order size.
    Date: 2020–07
  5. By: Peter Belcak; Peter Belcak; Jan-Peter Calliess; Stefan Zohren
    Abstract: We introduce a new software toolbox, called Multi-Agent eXchange Environment (MAXE), for agent-based simulation of limit order books. Offering both efficient C++ implementations and Python APIs, it allows the user to simulate large-scale agent-based market models while providing user-friendliness for rapid prototyping. Furthermore, it benefits from a versatile message-driven architecture that offers the flexibility to simulate a range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics. Showcasing its utility for research, we employ our simulator to investigate the influence the choice of the matching algorithm has on the behaviour of artificial trader agents in a zero-intelligence model. In addition, we investigate the role of the order processing delay in normal trading on an exchange and in the scenario of a significant price change. Our results include the findings that (i) the variance of the bid-ask spread exhibits a behavior similar to resonance of a damped harmonic oscillator with respect to the processing delay and that (ii) the delay markedly affects the impact a large trade has on the limit order book.
    Date: 2020–08
  6. By: Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School); Yajun Wang (University of Maryland)
    Abstract: We present a one-period model of oligopolistic strategic trading among symmetric traders who agree to disagree about the precision of their private signals. We derive several invariance relationships relating the number of firms, number of firm’s employees, average trade size, price impact, and pricing accuracy to dollar volume and returns volatility. Since a substantial part of order flow is often internalized within firms and does not reach the marketplace, invariance relationships can be modified to account for internalized order flow.
    Keywords: invariance, agreement to disagree, market power, trade size, price impact, pricing accuracy, volume, volatility, market microstructure, industrial organization
    Date: 2020–08
  7. By: Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School)
    Abstract: For five stock market crashes, we compare price declines with predictions from market microstructure invariance. During the 1987 crash and the 2008 sales by Société Générale, prices fell by magnitudes similar to predictions from invariance. Larger-than-predicted temporary price declines during 1987 and 2010 flash crashes suggest rapid selling exacerbates transitory price impact. Smaller-than-predicted price declines for the 1929 crash suggest slower selling stabilized prices and less integration made markets more resilient. Quantities sold in the three largest crashes indicate fatter tails or larger variance than the log-normal distribution estimated from portfolio transitions data.
    Keywords: Finance, market microstructure, invariance, crashes, liquidity, price impact, market depth, systemic risk
    JEL: G01 G28 N22
    Date: 2020–08
  8. By: Kyoung-hun Bae (Hanyang University); Albert S. Kyle (University of Maryland); Eun Jung Lee (Hanyang University); Anna A. Obizhaeva (New Economic School)
    Abstract: Market microstructure invariance predicts business time to unfold at a rate proportional to the 2~3 power of the product of dollar volume and returns volatility. Define a “switching point†as an investor changing the direction of trading from buying to selling or selling to buying. For a specific market, the aggregate number of switching points is a good indicator of the pace of business time. Using data from the Korea Exchange (KRX) from 2008 to 2010, we calculate the number of switching points for each stock for each month. The estimated exponent is 0.675 (standard error 0.005, R2 = 0.93) validates the business time clock predicted by invariance. Most variation reflects variation in the number of accounts trading a stock, not variation of switching points per account.
    Keywords: finance, market microstructure, asset pricing, invariance, trading volume, volatility, liquidity, price impact, market depth
    Date: 2020–08
  9. By: Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School)
    Abstract: This paper shows how to map predictions of theoretical models of market microstructure into operational empirical measures of liquidity. A meta-model implies an empirical measure of liquidity, denoted L, which describes various characteristics of trading and funding liquidity such as trading costs, bet sizes, haircuts, and capital requirements. When mapped into existingmodels of adverse selection, themeta-model also describes precisely how adverse selection shows up in pricing accuracy and resiliency. Themeta-model is consistent with models of both block trading and flow trading. It highlights a deep connection between time and adverse selection.
    Keywords: market microstructure, invariance, liquidity, adverse selection, market impact, bidask spread, bet size,market efficiency, dimensional analysis, leverage neutrality.
    JEL: G10 G12 G14 G20
    Date: 2020–07
  10. By: Albert S. Kyle (University of Maryland); Anna A. Obizhaeva (New Economic School)
    Abstract: Finding a universal market impact formula remains one of the most fascinating puzzles in finance. This paper reviews two possible approaches for imposing restrictions on this formula. First, restrictions can be obtained from a system of economic equations using trading volume and volatility, as suggested by Kyle and Obizhaeva (2017b). Second, restrictions can be derived using dimensional analysis and leverage neutrality, as suggested by Kyle and Obizhaeva (2017a). Except for the knife-edged case of the square root market impact function, additional assumptions related to market microstructure invariance are needed to apply the same market impact formula to all assets simultaneously. This results in a tightly parameterized universalmarket impact formula suitable for empirical testing.
    Keywords: Market microstructure, invariance, liquidity, square rootmodel, market impact, transaction costs, dimensional analysis, leverage neutrality, volume, volatility
    Date: 2020–08
  11. By: Sergey Kovbasyuk (New Economic School); Marco Pagano (University of Naples Federico II, CSEF and EIEF)
    Abstract: Arbitrageurs with a short investment horizon gain from accelerating price discovery by advertising their private information. However, advertising many assets may overload investors' attention, reducing the number of informed traders per asset and slowing price discovery. So arbitrageurs optimally concentrate advertising on just a few assets, which they overweight in their portfolios. Unlike classic insiders, advertisers prefer assets with the least noise trading. If several arbitrageurs share information about the same assets, inecient equilibria can arise, where investors' attention is overloaded and substantial mispricing persists. When they do not share, the overloading of investors' attention is maximal.
    Keywords: limits to arbitrage, advertising, price discovery, limited attention
    JEL: G11 G14 G2 D84
    Date: 2020–08
  12. By: Victor Olkhov
    Abstract: This paper presents probability distributions for price and returns random processes for averaging time interval {\Delta}. These probabilities determine properties of price and returns volatility. We define statistical moments for price and returns random processes as functions of the costs and the volumes of market trades aggregated during interval {\Delta}. These sets of statistical moments determine characteristic functionals for price and returns probability distributions. Volatilities are described by first two statistical moments. Second statistical moments are described by functions of second degree of the cost and the volumes of market trades aggregated during interval {\Delta}. We present price and returns volatilities as functions of number of trades and second degree costs and volumes of market trades aggregated during interval {\Delta}. These expressions support numerous results on correlations between returns volatility, number of trades and the volume of market transactions. Forecasting the price and returns volatilities depend on modeling the second degree of the costs and the volumes of market trades aggregated during interval {\Delta}. Second degree market trades impact second degree of macro variables and expectations. Description of the second degree market trades, macro variables and expectations doubles the complexity of the current macroeconomic and financial theory.
    Date: 2020–08
  13. By: Alessandra Casella (Columbia University [New York]); Antonin Macé (PSE - Paris School of Economics, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: Voters have strong incentives to increase their inuence by trading votes, a practice indeed believed to be common. But is vote trading welfare-improving or welfare-decreasing? We review the theoretical literature and, when available, its related experimental tests. We begin with the analysis of logrolling { the exchange of votes for votes, considering both explicit vote exchanges and implicit vote trades engineered by bundling issues in a single bill. We then focus on vote markets, where votes can be traded against a numeraire. We cover competitive markets, strategic market games, decentralized bargaining, and more centralized mechanisms, such as quadratic voting, where votes can be bought at a quadratic cost. We conclude with procedures allowing voters to shift votes across decisions { to trade votes with oneself only { such as storable votes or a modi_ed form of quadratic voting. We _nd that vote trading and vote markets are typically ine_cient; more encouraging results are obtained by allowing voters to allocate votes across decisions.
    Keywords: logrolling,vote trading,storable votes,quadratic voting,bundling,vote markets
    Date: 2020–08

This nep-mst issue is ©2020 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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