nep-mst New Economics Papers
on Market Microstructure
Issue of 2015‒06‒20
five papers chosen by
Thanos Verousis
University of Bath

  1. High-Frequency Trading and Market Performance‡ By Markus Baldauf; Joshua Mollner
  2. Trading in Fragmented Markets By Markus Baldauf; Joshua Mollner
  3. The importance of High Frequency Data on the Financial Markets By Simona Adascalitei
  4. Limits to arbitrage: The case of single stock futures and spot prices By Nidhi Aggarwal
  5. The informational role of algorithmic traders in the option market By Rohini Grover

  1. By: Markus Baldauf (Stanford University); Joshua Mollner (Stanford University)
    Abstract: High-frequency trading has transformed financial markets in recent years. We study the consequences of this development using a model with multiple trading venues, costly information acquisition, and several types of traders. An increase in trading speed crowds out information acquisition by reducing the gains from trading against mispriced quotes. Thus, faster speeds have two effects on traditional measures of market performance. First, the bid-ask spread declines, since there are fewer informational asymmetries. Second, price efficiency deteriorates, since less information is available to be incorporated into prices. A general tradeoff exists between low spreads and price efficiency. We characterize the frontier of this tradeoff and evaluate several trading mechanisms within this framework. The prevalent limit order book mechanism generally does not induce outcomes on this frontier. We consider two alternatives: first, a small delay added to the processing of all orders except cancellations, and second, frequent batch auctions. Both induce equilibrium outcomes on this frontier.
    Date: 2015–06
  2. By: Markus Baldauf (Stanford University); Joshua Mollner (Stanford University)
    Abstract: This paper applies an econometric model of imperfect competition to equity trading with competing exchanges. Stock of the same company is traded on multiple venues today. This development was driven by regulations, aimed at benefiting investors by fostering competition among exchanges. However, the welfare consequences of increased exchange competition are theoretically ambiguous. While competition does place down- ward pressure on the bid-ask spread, this force may be outweighed by increased adverse selection that stems from additional arbitrage opportunities. We investigate this ambi- guity empirically by estimating key parameters of the model using detailed trading data from Australia. The benefits of increased competition are outweighed by the costs of multi-venue arbitrage. Compared to the prevailing duopoly, we predict that the coun- terfactual spread under a monopoly would be 23 percent lower. Further, market design variations on the continuous limit order book would eliminate profits from cross-venue ar- bitrage strategies and reduce the spread by 51 percent. Finally, eliminating off-exchange trades, so-called dark trading, would reduce the spread by 11 percent.
    Date: 2015–06
  3. By: Simona Adascalitei (Romanian Academy Iasi Branch)
    Abstract: While the High Frequency Trading (HFT) activity is in decline and researchers have inconclusive results about its net (positive/negative) impact on the financial markets, massive quantities of High Frequency Data (HFD) become more and more accessible. The purpose of this paper is to highlight the importance of HFD on the Financial Markets. To support this statement, we will analyze some of the special characteristics of High Frequency Data (HFD) compared to the characteristics of Low Frequency Data (LFD). Then we will make a review of the papers that have proven that the use of HFD can improve the accuracy of volatility measures, volatility estimators, and volatility forecasts. Given this superiority of HFT over LFD, our aim is to encourage academics and practitioners to start focusing more on this type of data in order to have a better understanding of the highly dynamic financial markets.
    Keywords: High Frequenc Trading, High Frequency Data, Financial Markets, Volatility
    JEL: G10 G17
  4. By: Nidhi Aggarwal (Indira Gandhi Institute of Development Research)
    Abstract: Market frictions limit arbitrage, but these frictions affect different stocks differently. Using intraday data on a liquid single stock futures and spot market, we examine the arbitrage efficiency of these two markets. We find evidence of significant cross- sectional variation in the size and asymmetricity of no-arbitrage bands. To the extent that market frictions affect all stocks similarly, commonality in the size of the bands is expected. 17 of variation in the size of the bands is explained by the first principal component. Changes in funding liquidity is a key factor that determines variation in the common component.
    Keywords: Limits to arbitrage, mispricing, no-arbitrage bands, short-selling constraints, transactions costs, funding constraints
    JEL: G13 G14
    Date: 2015–05
  5. By: Rohini Grover (Indira Gandhi Institute of Development Research)
    Abstract: This paper investigates the information role of algorithmic traders (AT) in the Nifty index option market. I analyse a unique dataset to test for information-based trading by looking at the effect of net buying pressure options on implied volatilities. According to the direction-learning hypothesis, (directional) informed investors' net buying pressure of calls (puts) raises the implied volatilities of calls (puts) and lowers the implied volatilities of puts (calls). In addition, their net buying pressure can also predict future index returns. According to the volatility-learning hypothesis, (volatility) informed investors' net buying pressure is always positively related to implied volatilities. I find that these relations do not hold for AT and, therefore, infer absense of information-based trading by AT. On the contrary, I find the direction-learning hypothesis to hold for non-AT who, in this market, are primarily individual investors.
    Keywords: mplied Volatility; Net buying pressure; Index option market
    JEL: G13 G14
    Date: 2015–05

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