nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒02‒10
five papers chosen by
Thanos Verousis
University of Essex

  1. Marked point processes and intensity ratios for limit order book modeling By Ioane Muni Toke; Nakahiro Yoshida
  2. Does Index Arbitrage Distort the Market Reaction to Shocks? By Stanislav Anatolyev; Sergei Seleznev; Veronika Selezneva
  3. Liquidity, Volume, and Volatility By Vincent Bogousslavsky; Pierre Collin-Dufresne
  4. Option Trading and Stock Price Informativeness By Jie Cao; Amit Goyal; Sai Ke; Xintong Zhan
  5. How Does Information Affect Liquidity in Over-the-Counter Markets? By Michael Junho Lee; Antoine Martin

  1. By: Ioane Muni Toke; Nakahiro Yoshida
    Abstract: This paper extends the analysis of Muni Toke and Yoshida (2020) to the case of marked point processes. We consider multiple marked point processes with intensities defined by three multiplicative components, namely a common baseline intensity, a state-dependent component specific to each process, and a state-dependent component specific to each mark within each process. We show that for specific mark distributions, this model is a combination of the ratio models defined in Muni Toke and Yoshida (2020). We prove convergence results for the quasi-maximum and quasi-Bayesian likelihood estimators of this model and provide numerical illustrations of the asymptotic variances. We use these ratio processes in order to model transactions occuring in a limit order book. Model flexibility allows us to investigate both state-dependency (emphasizing the role of imbalance and spread as significant signals) and clustering. Calibration, model selection and prediction results are reported for high-frequency trading data on multiple stocks traded on Euronext Paris. We show that the marked ratio model outperforms other intensity-based methods (such as "pure" Hawkes-based methods) in predicting the sign and aggressiveness of market orders on financial markets.
    Date: 2020–01
  2. By: Stanislav Anatolyev; Sergei Seleznev; Veronika Selezneva
    Abstract: We show that ETF arbitrage distorts the market reaction to fundamental shocks. We confirm this hypothesis by creating a new measure of the intensity of arbitrage transactions at the individual stock level and using an event study analysis to estimate the market reaction to economic shocks. Our measure of the intensity of arbitrage is the probability of simultaneous trading of ETF shares with shares of underlying stocks estimated using high frequency data. Our approach is direct, and it accounts for statistical arbitrage, passive investment strategies, and netting of arbitrage positions over the day, which the existing measures cannot do. We conduct several empirical tests, including the use of a quasi-natural experiment, to confirm that our measure captures uctuations in the intensity of arbitrage transactions. We focus on oil shocks because they contain a large idiosyncratic component which facilitates identication of our mechanism and interpretation of the results. Oil shocks are identified using weekly oil inventory announcements.
    Keywords: high-frequency data; stock market; ETF; arbitrage intensity; oil shock; market efficiency;
    JEL: G12 G14 G23 Q43
    Date: 2019–12
  3. By: Vincent Bogousslavsky (Boston College - Department of Finance); Pierre Collin-Dufresne (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute; National Bureau of Economic Research (NBER))
    Abstract: We examine the relation between liquidity, volume, and volatility using a comprehensive sample of U.S. stocks in the post-decimalization period. For large stocks, effective spread and volume are positively related in the time series even after controlling for volatility, contrary to most theoretical predictions. This relation is mostly driven by the systematic component of volume. In contrast, for small stocks the evidence matches the predictions of standard adverse selection models. In line with a continuous-time inventory model, we show that the volatility of order imbalances can reconcile our puzzling finding with standard intuition. Order imbalance volatility is strongly associated with spreads both in the time series and cross-section. Evidence from alternative liquidity measures (price impact and depth), spread decomposition, and intraday patterns support our interpretation of order imbalance volatility as a measure of inventory risk. Furthermore, order imbalance volatility is priced in the cross-section of weekly returns.
    Keywords: liquidity, volume, volatility, order imbalance, inventory, adverse selection
    JEL: G10 G12 G14
    Date: 2019–03
  4. By: Jie Cao (The Chinese University of Hong Kong (CUHK) - CUHK Business School); Amit Goyal (University of Lausanne; Swiss Finance Institute); Sai Ke (University of Houston - C.T. Bauer College of Business); Xintong Zhan (The Chinese University of Hong Kong (CUHK) - CUHK Business School)
    Abstract: We examine the impact of single-name option trading on stock price informativeness. By documenting a robust relation and establishing causality, we confirm that option trading causes the stock price to incorporate more firm-specific information. Our findings are through the channels of investors’ acquiring more information and through managers’ voluntary release. The findings are driven by firms with higher information asymmetry and firms with more efficiently priced options.
    Keywords: option trading, price informativeness, stock synchronicity, information acquisition and production
    JEL: G02 G12 G13 G14
    Date: 2019–06
  5. By: Michael Junho Lee; Antoine Martin
    Abstract: A large volume of financial transactions occur in decentralized markets that commonly depend on a network of dealers. Dealers face two impediments to providing liquidity in these markets. First, dealers may face informed traders. Second, they may face costs associated with maintaining large balance sheets, either due to inventory or liquidity costs. In a recent paper, we study a model of over-the-counter (OTC) markets in which liquidity is endogenously determined by dealers who must contend with both asymmetric information and liquidity costs. This post provides an intuitive explanation of our model and the dynamics of interdealer liquidity.
    Keywords: Liquidity; information; inter-dealer
    JEL: G1 G14
    Date: 2020–01–13

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