nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒04‒23
seven papers chosen by
Thanos Verousis

  1. Scarcity effects of QE: A transaction-level analysis in the Bund market By Kathi Schlepper; Heiko Hofer; Ryan Riordan; Andreas Schrimpf
  2. High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed By F. Lilla
  3. Bid-Ask Spread Determination in the FX Swap Market: Competition, Collusion or a Convention? By Alexis Stenfors; ;
  4. Belief-free Price Formation By Hörner, Johannes; Lovo, Stefano
  5. Is Post-Crisis Bond Liquidity Lower? By Mike Anderson; René M. Stulz
  6. An empirical behavioural order-driven model with price limit rules By Gao-Feng Gu; Xiong Xiong; Hai-Chuan Xu; Wei Zhang; Yong-Jie Zhang; Wei Chen; Wei-Xing Zhou
  7. Measuring Transaction Costs in the Absence of Timestamps By Filip Zikes

  1. By: Kathi Schlepper; Heiko Hofer; Ryan Riordan; Andreas Schrimpf
    Abstract: This paper investigates the scarcity effects of quantitative easing (QE) policies, drawing on intra-day transaction-level data for German government bonds, purchased under the public sector purchase program (PSPP) of the ECB/Eurosystem. This paper is the first to match high-frequency QE purchase data with high-frequency inter-dealer data. We find economically significant price impacts at high (minute-by-minute) and low (daily) frequencies, highlighting the relevance of scarcity effects in bond markets. Asset purchase policies are not without side effects, though, as the induced scarcity has an adverse impact on liquidity conditions as measured by bid-ask spreads and inter-dealer order book depth. We further show that the price impact varies greatly with market conditions: it is considerably higher during episodes of illiquidity and when yields are higher.
    Keywords: Easing, European Central Bank, Scarcity Channel, Bond Market Liquidity, High-Frequency Data
    Date: 2017–04
  2. By: F. Lilla
    Abstract: Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism and the existence of bid-ask spread. Moreover, these data are not always available and, even if they are, the asset’s liquidity may be not sufficient to allow for frequent transactions. This paper considers different variants of these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumps in prices and leverage effects for volatility. Findings suggest that daily-data models are preferred to HF-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing fat-tails) and leverage effects translates in more accurate VaR measure.
    JEL: C58 C53 C22 C01 C13
    Date: 2017–04
  3. By: Alexis Stenfors (Portsmouth Business School); ;
    Abstract: Following a series of manipulation scandals in the global foreign exchange and money markets, recent lawsuits, regulatory reform proposals and bank compliance changes have explicitly targeted anti-competitive behaviour. By empirically investigating the determination of bid-ask spreads in foreign exchange swap markets, this paper addresses some contradictions where reciprocality, trust and conventions remain fundamental and logical. By doing so, the paper critically reflects upon the bid-ask spread in traditional market microstructure theory, the definition of the spread as a ‘component of price’ in antitrust law, and the policy implications in light of the recent regulatory investigations into financial benchmarks and OTC markets.
    Keywords: Foreign exchange, LIBOR, market microstructure theory, bid-ask spreads, collusion, OTC markets
    JEL: D4 F3 G1 G3 K2
    Date: 2017–04–18
  4. By: Hörner, Johannes; Lovo, Stefano
    Abstract: We analyze security price formation in a dynamic setting in which long-lived dealers repeatedly compete for trading with potentially informed retail traders. For a class of market microstructure models, we characterize equilibria in which dealers’ dynamic pricing strategies are optimal no matter the private information each dealer may possess. In a generalized version of the Glosten and Milgrom model, these equilibria deliver price dynamics reminiscent of well-known stylized facts: price/trading-flow correlation, volatility clustering, price bubble and inventory/inter-dealer trading correlation.
    Keywords: Financial Market Microstructure, Belief-free Equilibria, Informed Market Makers, Price Volatility.
    JEL: C72 C73 G1 G12
    Date: 2017–03
  5. By: Mike Anderson; René M. Stulz
    Abstract: Price-based liquidity metrics are much better for small trades after the crisis than before the crisis. For large trades, these metrics are generally worse from 2010 to 2012 and better from 2013 to 2014 than from 2004 to 2006. However, turnover falls sharply after the crisis, which is consistent with investors having more difficulty completing trades on acceptable terms. A frequent concern is that post-crisis liquidity could be low when markets are stressed. We consider three stress events: extreme VIX increases, extreme bond yield increases, and downgrades to high yield. We find evidence that liquidity is lower after the crisis for extreme VIX increases. However, we find no evidence that liquidity related to idiosyncratic stress events is worse after the crisis than before the crisis. Our results emphasize the importance of considering how liquidity reacts to shocks which can affect financial stability and of taking into account the information from non-price liquidity metrics.
    JEL: G12 G18 G28
    Date: 2017–04
  6. By: Gao-Feng Gu; Xiong Xiong; Hai-Chuan Xu; Wei Zhang; Yong-Jie Zhang; Wei Chen; Wei-Xing Zhou
    Abstract: We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order directions, the long memory in relative prices, the asymmetric distribution of relative prices, and the nonlinear dependence of the average order size and its standard deviation on the relative price. Order cancellation follows a Poisson process with the arrival rate determined from real data and the cancelled order is determined according to the empirical distributions of relative price level and relative position at the same price level. All these ingredients of the model are derived based on the empirical microscopic regularities in the order flows of stocks on the Shenzhen Stock Exchange. The model is able to produce the main stylized facts in real markets. Computational experiments uncover that asymmetric setting of price limits will cause the stock price diverging exponentially when the up price limit is higher than the down price limit and vanishing vice versus. We also find that asymmetric price limits have influences on stylized facts. Our EBOD model provides a suitable computational experiment platform for academics, market participants and policy makers.
    Date: 2017–04
  7. By: Filip Zikes
    Abstract: This paper develops measures of transaction costs in the absence of transaction timestamps and information about who initiates transactions, which are data limitations that often arise in studies of over-the-counter markets. I propose new measures of the effective spread and study the performance of all estimators analytically, in simulations, and present an empirical illustration with small-cap stocks for the 2005-2014 period. My theoretical, simulation, and empirical results provide new insights into measuring transaction costs and may help guide future empirical work.
    Keywords: Effective spread ; Simulated method of moments ; Time-varying estimation ; Transaction costs
    JEL: C14 C15 G20
    Date: 2017–04–06

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