nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒05‒14
four papers chosen by
Thanos Verousis
University of Newcastle

  1. Algorithmic Trading Behaviour and High-Frequency Liquidity Withdrawal in the FX Spot Market By Alexis Stenfors; Masayuki Susai; ;
  2. Benchmark Dataset for Mid-Price Prediction of Limit Order Book data By Adamantios Ntakaris; Martin Magris; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  3. Portfolio Choice with Small Temporary and Transient Price Impact By Ibrahim Ekren; Johannes Muhle-Karbe
  4. A coupled component GARCH model for intraday and overnight volatility By Oliver Linton; Jianbin Wu

  1. By: Alexis Stenfors (Portsmouth Business School); Masayuki Susai (Nagasaki University); ;
    Abstract: This paper studies the frequency and speed of limit order cancellations in the FX spot market for EUR/USD, USD/JPY and EUR/JPY. By investigating both ‘market-specific’ and ‘order-specific’ drivers of liquidity withdrawal, we report several findings that could serve to question traditional market microstructure theory as well as conventional anecdotes from financial market participants. Overall, it appears as if limit orders with characteristics more likely to be submitted by algorithmic traders are perceived to be more informed or predatory than orders submitted human traders - thus acting to trigger more, and faster, limit order cancellations.
    Keywords: market microstructure, limit order book, foreign exchange, high-frequency trading
    JEL: D4 F3
    Date: 2017–05–02
  2. By: Adamantios Ntakaris; Martin Magris; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: Presently, managing prediction of metrics in high frequency financial markets is a challenging task. An efficient way to do it is by monitoring the dynamics of a limit order book and try to identify the information edge. This paper describes a new benchmark dataset of high-frequency limit order markets for mid-price prediction. We make publicly available normalized representations of high frequency data for five stocks extracted from the NASDAQ Nordic stock market. Furthermore, we define an experimental protocol that can be used in order to evaluate the performance of related research methods. Baseline results based on linear and nonlinear regression models are also provided and show the potential that these methods have for mid-price prediction.
    Date: 2017–05
  3. By: Ibrahim Ekren; Johannes Muhle-Karbe
    Abstract: We study portfolio selection in a model with both temporary and transient price impact introduced by Garleanu and Pedersen [24]. In the large-liquidity limit where both frictions are small, we derive explicit formulas for the asymptotically optimal trading rate and the corresponding minimal leading-order performance loss. We find that the losses are governed by the volatility of the frictionless target strategy, like in models with only temporary price impact. In contrast, the corresponding optimal portfolio not only tracks the frictionless optimizer, but also exploits the displacement of the market price from its unaffected level.
    Date: 2017–05
  4. By: Oliver Linton (Institute for Fiscal Studies and University of Cambridge); Jianbin Wu (Institute for Fiscal Studies)
    Abstract: We propose a semi-parametric coupled component GARCH model for intraday and overnight volatility that allows the two periods to have di fferent properties. To capture the very heavy tails of overnight returns, we adopt a dynamic conditional score model with t innovations. We propose a several step estimation procedure that captures the nonparametric slowly moving components by kernel estimation and the dynamic parameters by t maximum likelihood. We establish the consistency and asymptotic normality of our estimation procedures. We extend the modelling to the multivariate case. We apply our model to the study of the Dow Jones industrial average component stocks over the period 1991-2016 and the CRSP cap based portfolios over the period of 1992-2015. We show that actually the ratio of overnight to intraday volatility has increased in importance for big stocks in the last 20 years. In addition, our model provides better intraday volatility forecast since it takes account of the full dynamic consequences of the overnight shock and previous ones.
    Date: 2017–01–26

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