nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒01‒20
five papers chosen by
Thanos Verousis

  1. Market Price of Trading Liquidity Risk and Market Depth By Masaaki Kijima; Christopher Ting
  2. A simple microstructural explanation of concave pice impact By Sergey Nadtochiy
  3. To snipe or not to snipe, that is the question! Transitions in sniping behaviour among competing algorithmic traders By Somayeh Kokabisaghi; Eric J Pauwels; Andre B Dorsman
  4. Investors' Trading Behaviour and Stock Market Volatility during Crisis Periods: A Dual Long-Memory Model for the Korean Stock Exchange By Guglielmo Maria Caporale; Menelaos Karanasos; Stavroula Yfanti; Aris Kartsaklas
  5. The cost of clearing fragmentation By Evangelos Benos; Wenqian Huang; Albert Menkveld; Michalis Vasios

  1. By: Masaaki Kijima; Christopher Ting
    Abstract: Price impact of a trade is an important element in pre-trade and post-trade analyses. We introduce a framework to analyze the market price of liquidity risk, which allows us to derive an inhomogeneous Bernoulli ordinary differential equation. We obtain two closed form solutions, one of which reproduces the linear function of the order flow in Kyle (1985) for informed traders. However, when traders are not as asymmetrically informed, an S-shape function of the order flow is obtained. We perform an empirical intra-day analysis on Nikkei futures to quantify the price impact of order flow and compare our results with industry's heuristic price impact functions. Our model of order flow yields a rich framework for not only to estimate the liquidity risk parameters, but also to provide a plausible cause of why volatility and correlation are stochastic in nature. Finally, we find that the market depth encapsulates the market price of liquidity risk.
    Date: 2019–12
  2. By: Sergey Nadtochiy
    Abstract: This article describes a simple model of market microstructure which explains a concave price impact. In the proposed model, the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the model is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions of the model, one of which is tested using publicly available market data without the information about meta-orders.
    Date: 2020–01
  3. By: Somayeh Kokabisaghi; Eric J Pauwels; Andre B Dorsman
    Abstract: In this paper we re-analyse the transition from sure to probabilistic sniping as explored in Menkveld and Zoican [14]. In that paper, the authors introduce a stylized version of a competitive game in which high frequency traders (HFTs) interact with each other and liquidity traders. The authors show that risk aversion plays an important role in the transition from sure to mixed (or probabilistic) sniping. In this paper, we re-interpret and extend these conclusions in the context of repeated games and highlight some differences in results. In particular, we identify situations in which probabilistic sniping is genuinely profitable that are qualitatively different from the ones obtained in [14]. Keywords: algorithmic trading,sniping, electronic exchange,high-frequency traders,Nash equilibrium,repeated games,bandits, subgame-perfect equilibrium,transition
    Date: 2019–12
  4. By: Guglielmo Maria Caporale; Menelaos Karanasos; Stavroula Yfanti; Aris Kartsaklas
    Abstract: This study examines the impact of investors’ buy and sell trades on Korean stock market volatility across two crisis events, the Asian crisis of 1997 and the 2008 global financial crash. We investigate the trading behaviour of domestic vs. foreign and institutional vs. individual investors. Our results suggest that the buy and sell trades have an asymmetric effect on volatility that depends on the type of investor trading and on the phase of the business cycle. Buy orders appear to be more informative than sell orders since they mostly lower volatility in the pre-crisis periods, while sell and post-crisis buy trades affect volatility positively regardless of who trades (institutional or individual investors) and on what information (member, non-member). Most importantly, decomposing total buy and sell trades into trader-type categories reveals that some institutional investors are more informed traders that stabilize the market compared to individuals that always increase volatility. Foreign investors reduce volatility with their purchases and total trading activity in the whole Asian crisis sample, but only in the pre-crisis period before the recent global financial turmoil.
    Keywords: financial crisis, foreign investors, individual investors, institutional investors, long memory, range-based volatility, structural change, trading volume
    JEL: G01 G12 G15 G23
    Date: 2019
  5. By: Evangelos Benos; Wenqian Huang; Albert Menkveld; Michalis Vasios
    Abstract: Fragmenting clearing across multiple central counterparties (CCPs) is costly. This is because dealers providing liquidity globally, cannot net trades cleared in different CCPs and this increases their collateral costs. These costs are then passed on to their clients through price distortions which take the form of a price differential (basis) when the same products are cleared in different CCPs. Using proprietary data, we document an economically significant CCP basis for U.S. dollar swap contracts cleared both at the Chicago Mercantile Exchange (CME) and the LCH in London and provide evidence consistent with a collateral cost explanation of this basis.
    Keywords: central clearing, CCP basis, collateral, fragmentation
    JEL: G10 G12 G14
    Date: 2019–12

This nep-mst issue is ©2020 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.