nep-mst New Economics Papers
on Market Microstructure
Issue of 2015‒04‒25
eight papers chosen by
Thanos Verousis
University of Bath

  1. Need for Speed? Exchange Latency and Liquidity By Albert J. Menkveld; Marius A. Zoican
  2. Intraday Price Discovery in Fragmented Markets By Sait Ozturk; Michel van der Wel
  3. Challenges posed by the evolution of the Treasury market By Potter, Simon M.
  4. Auction versus Dealership Markets: Impact of Proprietary Trading with Transaction Fees By Katsumasa Nishide; Yuan Tian
  5. The Determinants of CDS Bid-ask Spreads By Marcin Wojtowicz
  6. Transitions in the Stock Markets of the US, UK, and Germany By Matthias Raddant; Friedrich Wagner
  7. Realized Volatility Risk By David E. Allen; Michael McAleer; Marcel Scharth
  8. Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions By Siem Jan Koopman; Rutger Lit; André Lucas

  1. By: Albert J. Menkveld (VU University Amsterdam); Marius A. Zoican (VU University Amsterdam)
    Abstract: Speeding up the exchange does not necessarily improve liquidity. The price quotes of high-frequency market makers are more likely to meet speculative high-frequency "bandits", thus less likely to meet liquidity traders. The bid-ask spread is raised in response. The recursive dynamic model reveals that there is an additional spread-widening effect as market makers earn higher rents due to economies of scope from quote monitoring. Analysis of a NASDAQ-OMX speed upgrade provides supportive evidence.
    Keywords: market microstructure, trading speed, information asymmetry, high-frequency trading
    JEL: G11 G12 G14
    Date: 2014–07–28
  2. By: Sait Ozturk (Econometric Institute, Erasmus University Rotterdam); Michel van der Wel (Econometric Institute, Erasmus University Rotterdam)
    Abstract: For many assets, trading is fragmented across multiple exchanges. Price discovery measures summarize the informativeness of trading on each venue for discovering the asset’s true underlying value. We explore intraday variation in price discovery using a structural model with time-varying parameters that can be estimated with state space techniques. An application to the Expedia stock demonstrates intraday variation, to the extent that the overall dominant trading venue (NASDAQ) does not lead the entire day. Spreads, the number of trades and volatility can explain almost half of the intraday variation in information shares.
    Keywords: High-frequency data, Market microstructure, Price Discovery, Kalman filter
    JEL: C32 G14
    Date: 2014–02–27
  3. By: Potter, Simon M. (Federal Reserve Bank of New York)
    Abstract: Remarks at the 2015 Primary Dealer Meeting, New York City.
    Keywords: Treasury market structure; Treasury Market Practices Group (TMPG); interdealer Treasury market; electronic trading; Automated trading; High-frequency trading (HFT); Central Limit Order Book (CLOB); Fixed Income Clearing Corporation (FICC); October 15 (2014)
    JEL: E52
    Date: 2015–04–13
  4. By: Katsumasa Nishide (Department of Economics, Yokohama National University); Yuan Tian (Faculty of Economics, Ryukoku University)
    Abstract: In this study, we consider a one-period financial market with a monopolistic dealer/broker and an infinite number of investors. While the dealer who trades on his own account (with proprietary trading) simultaneously sets both the transaction fee and the asset price, the broker who brings investors’ orders to the market (with no proprietary trading) sets only the transaction fee, given that the price is determined according to the marketclearing condition among investors. We analyze the impact of proprietary trading on the asset price, transaction fee, trading volume, and the welfare of investors. Results show that proprietary trading increases both the trading volume and the transaction fee, and improves social welfare. Our study effectively demonstrates how proprietary trading affects market equilibrium and welfare of investors.
    Keywords: Proprietary trading, dealer market, brokered market, transaction fees
    JEL: D53 G12 D42
    Date: 2015–04
  5. By: Marcin Wojtowicz (VU University Amsterdam, the Netherlands)
    Abstract: We investigate the determinants of bid-ask spreads on corporate credit default swaps (CDSs). We find that proxies for dealer inventory costs such as variability of CDS premia and CDS trading volume explain as much as 80% of variation in CDS bid-ask spreads. We also analyze the influence of variables capturing systematic risk of reference entities, market-implied volatility, dealer funding costs and competition between dealers. Several of these variables are significant, but their explanatory power is moderate. Finally, we demonstrate that CDS bid-ask spreads do not widen preceding earnings announcement surprises, which suggests that private information does not hinder CDS liquidity.
    Keywords: Credit default swaps, Liquidity, Bid-ask spreads, Components of bid-ask spreads
    JEL: G10 G14 G19
    Date: 2014–10–20
  6. By: Matthias Raddant; Friedrich Wagner
    Abstract: In an analysis of the US, the UK, and the German stock market we find a change in the behavior based on the stock's beta values. Before 2006 risky trades were concentrated on stocks in the IT and technology sector. Afterwards risky trading takes place for stocks from the financial sector. We show that an agent-based model can reproduce these changes. We further show that the initial impulse for the transition might stem from the increase of high frequency trading at that time.
    Date: 2015–04
  7. By: David E. Allen (Edith Cowan University, Australia); Michael McAleer (Erasmus University Rotterdam, and Complutense University of Madrid); Marcel Scharth (University of New South Wales, Australia)
    Abstract: This discussion paper led to an article in the <I>Journal of Risk and Financial Management</I> (2014). Volume 7(2), pages 80-109.<P> In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Keywords: Realized volatility, volatility of volatility, volatility risk, value-at-risk, forecasting,
    JEL: C14 C22 C58 G15
    Date: 2013–07–16
  8. By: Siem Jan Koopman (VU University Amsterdam, the Netherlands); Rutger Lit (VU University Amsterdam, the Netherlands); André Lucas (VU University Amsterdam, the Netherlands)
    Abstract: We investigate the intraday dependence pattern between tick data of stock price changes using a new time-varying model for discrete copulas. We let parameters of both the marginal models and the copula vary over time using an observation driven autoregressive updating scheme based on the score of the conditional probability mass function with respect to the time-varying parameters. We apply the model to high-frequency stock price changes expressed as discrete tick-size multiples for four liquid U.S. financial stocks. Our modeling framework is based on Skellam densities for the marginals and a range of different copula functions. We find evidence of intraday time-variation in the dependence structure. After the opening and before the close of the stock market, dependence levels are lower. We attribute this finding to more idiosyncratic trading at these times. The introduction of score driven dynamics in the dependence structure significantly increases the likelihood values of the time-varying copula model. By contrast, a fixed daily seasonal dependence pattern clearly fits the data less well.
    Keywords: time-varying copulas, dynamic discrete data, score driven models, Skellam distribution, dynamic dependence
    JEL: C32 G11
    Date: 2015–03–19

This nep-mst issue is ©2015 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.