nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒04‒16
seven papers chosen by
Thanos Verousis

  1. Anomalous Trading Prior to Lehman Brothers' Failure By Gehrig, Thomas; Haas, Marlene
  2. Dealer Trading at the Fix By Carol Osler; ;
  3. Information Asymmetry and Information Dissemination in High-Frequency Capital Markets By Pöppe, Thomas
  4. Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model By Xiaohong Chen; Oliver Linton; Stefan Schneeberger; Yanping Yi
  5. Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence By Markus Bibinger; Nikolaus Hautsch; Peter Malec; Markus Reiss
  6. Microstructure under the Microscope: Combining Dimension Reduction, Distance Measures and Covariance By Ravi Kashyap
  7. Herding behavior and volatility clustering in financial markets By Schmitt, Noemi; Westerhoff, Frank

  1. By: Gehrig, Thomas; Haas, Marlene
    Abstract: We study price discovery during the liquidity freeze of September 2008, when fundamental values were difficult to be assessed. We find that trading volume and trade size significantly increased two days before the public announcement of Lehman's lethal quarter loss. Nevertheless, informational risk as perceived by liquidity suppliers increased only after the public disclosure of this loss. The price impact of trades was minimal and stock markets kept on working efficiently for Lehman stocks until the insolvency announcement. Price efficiency is on average established after half a second, which could have been exploited by low-latency traders.
    Keywords: low latency trading; price discovery; price impact; trading volume
    JEL: G00 G14 N00 N2
    Date: 2016–03
  2. By: Carol Osler (Brandeis University); ;
    Abstract: This paper analyzes dealer trading at "fixes," which are benchmark financial prices set at specific times of day. Extreme returns and quick retracements are common around fixes and often prompt suspicions of collusion and market manipulation, but the connections between price dynamics and dealer behavior are poorly understood. I examine a model of trading at the fix in which dealers can engage in three prohibited behaviors: front-running, sharing information about customer orders, and colluding. The model shows that dealers will engage a strategy akin to front-running regardless of whether they compete or collude, causing quick retracements after the fix. Collusion shuts down free-riding among dealers while information sharing intensifies it. Therefore collusion intensifies, and information-sharing reduces, pre-fix volatility, post-fix retracements, and the convexity of the pre-fix price path.
    Date: 2016–03
  3. By: Pöppe, Thomas
    Abstract: This dissertation is concerned with information asymmetry and information dissemination in high-frequency capital markets. At the intersection of information dissemination and asymmetry with market microstructure, this dissertation pursues three major goals. We propose enhancements to market microstructure methodology to be able to empirically conduct research on information dissemination and asymmetry on recent, high-frequency trading data. Second, we empirically evaluate related microstructure methodology to test its robustness and guide researchers in its application. Third, we employ the proposed methodology to evaluate the efficacy of different information channels, both traditional, legislation-based and new, technology-based channels.
    Date: 2016
  4. By: Xiaohong Chen (Cowles Foundation, Yale University); Oliver Linton (University of Cambridge); Stefan Schneeberger (Dept. of Economics, Yale University); Yanping Yi (Shanghai University of Finance and Economics - School of Economics)
    Abstract: We propose new methods for estimating the bid-ask spread from observed transaction prices alone. Our methods are based on the empirical characteristic function instead of the sample autocovariance function like the method of Roll (1984). As in Roll (1984), we have a closed form expression for the spread, but this is only based on a limited amount of the model-implied identification restrictions. We also provide methods that take account of more identification information. We compare our methods theoretically and numerically with the Roll method as well as with its best known competitor, the Hasbrouck (2004) method, which uses a Bayesian Gibbs methodology under a Gaussian assumption. Our estimators are competitive with Roll's and Hasbrouck's when the latent true fundamental return distribution is Gaussian, and perform much better when this distribution is far from Gaussian. Our methods are applied to the E-mini futures contract on the S&P 500 during the Flash Crash of May 6, 2010. Extensions to models allowing for unbalanced order flow or Hidden Markov trade direction indicators or trade direction indicators having general asymmetric support or adverse selection are also presented, without requiring additional data.
    Keywords: Characteristic function, Deconvolution, Flash Crash, Liquidity
    JEL: C30 C32 G10
    Date: 2016–03
  5. By: Markus Bibinger; Nikolaus Hautsch; Peter Malec; Markus Reiss
    Abstract: We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
    Keywords: local method of moments, spot covariance, smoothing, intraday (co-)variation risk
    JEL: C58 C14 C32
    Date: 2014–10–07
  6. By: Ravi Kashyap
    Abstract: Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. In financial markets, where there is an abundance of recorded information, this translates to the study of the dynamic relationships between observed variables, such as price, volume and spread, and hidden constituents, such as transaction costs and volatility, that hold sway over the efficient functioning of the system. We consider a measure of similarity, the Bhattacharyya distance, across distributions of these variables. We illustrate a novel methodology based on the marriage between the Bhattacharyya distance and the Johnson Lindenstrauss Lemma, a technique for dimension reduction, providing us with a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. We demonstrate a relationship between covariance and distance measures based on a generic extension of Stein's Lemma. The degree to which different markets or sub groups of securities have different measures of their corresponding distributions tells us the extent to which they are different. This can aid investors looking for diversification or looking for more of the same thing. We briefly discuss how this methodology lends itself to numerous Marketstructure studies and even applications outside the realm of finance / social sciences.
    Date: 2016–03
  7. By: Schmitt, Noemi; Westerhoff, Frank
    Abstract: We propose a simple agent-based financial market model in which speculators follow a linear mix of technical and fundamental trading rules to determine their orders. Volatility clustering arises in our model due to speculators' herding behavior. In case of heightened uncertainty, speculators observe other speculators' actions more closely. Since speculators' trading behavior then becomes less heterogeneous, the market maker faces a less balanced excess demand and consequently adjusts prices more strongly. Estimating our model using the method of simulated moments reveals that it is able to explain a number of stylized facts of financial markets quite well. Keywords: Agent-based financial market models, stylized facts of financial markets, technical and fundamental analysis, heterogeneity, herding behavior, method of simulated moments.
    JEL: C63 D84 G15
    Date: 2016

This nep-mst issue is ©2016 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.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.