nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒10‒22
five papers chosen by
Thanos Verousis

  1. Price Discovery in the Stock Index Futures Market: Evidence from the Chinese stock market crash By Hou, Yang; Nartea, Gilbert
  2. Navigating dark liquidity (How Fisher catches Poisson in the Dark) By Ilija I. Zovko
  3. Dynamic asset price jumps and the performance of high frequency tests and measures By Worapree Maneesoonthorn; Gael M. Martin; Catherine S. Forbes
  4. Market impact with multi-timescale liquidity By Michael Benzaquen; Jean-Philippe Bouchaud
  5. Time-Varying Price Discovery and Autoregressive Loading Factors: Evidence from S&P 500 Cash and E-Mini Futures Markets By Hou, Yang; Li, Steven

  1. By: Hou, Yang; Nartea, Gilbert
    Abstract: This paper examines time-varying price discovery of the Chinese stock index futures market during a stock market crash in 2015. We find that the index futures market plays a long-run leading role in terms of its higher static and dynamic generalised information share (GIS) than both the Shanghai and Shenzhen A share markets during the market turbulence. The expected trading volume in each market improves GIS of that market. The importance of trading activities by the majority of investors in increasing market efficiency during a crash is underscored. Government intervention on futures trading impairs price discovery in the futures market.
    Keywords: Generalised Information Share, Price Discovery, GARCH model, Chinese stock market crash, Chinese stock index futures
    JEL: G13 G14 G15
    Date: 2017–10–17
  2. By: Ilija I. Zovko
    Abstract: In order to reduce signalling, traders may resort to limiting access to dark venues and imposing limits on minimum fill sizes they are willing to trade. However, doing this also restricts the liquidity available to the trader since an ever increasing quantity of orders are traded by algos in clips. An alternative is to attempt to monitor signalling in real time and dynamically make adjustments to the dark liquidity accessed. In practice, price slippage against the order is commonly taken as an indication of signalling. However, estimating slippage is difficult and requires a large number of fills to reliably detect it. Ultimately, even if detected, it fails to capture an important element of causality between dark fills and lit prints - a signature of information leakage. In the extreme, this can lead to scaling back trading at a time when slippage is caused by a competing trader consuming liquidity, and the appropriate action would be to scale trading up -- not down -- in order to capture good prices. In this paper we describe a methodology aimed to address this dichotomy of trading objectives, allowing to maximally capture available liquidity while at the same time protecting the trader from excessive signalling. The method is designed to profile dark liquidity in a dynamic fashion, on a per fill basis, in contrast to historical venue analyses based on estimated slippage. This allows for a dynamic and real-time control of the desired liquidity exposure.
    Date: 2017–10
  3. By: Worapree Maneesoonthorn; Gael M. Martin; Catherine S. Forbes
    Abstract: This paper provides an extensive evaluation of high frequency jump tests and measures, in the context of dynamic models for asset price jumps. Specifically, we investigate: i) the power of alternative tests to detect individual price jumps, including in the presence of volatility jumps; ii) the frequency with which sequences of dynamic jumps are identified; iii) the accuracy with which the magnitude and sign of sequential jumps are estimated; and iv) the robustness of inference about dynamic jumps to test and measure design. Substantial differences are discerned in the performance of alternative methods in certain dimensions, with inference being sensitive to these differences in some cases. Accounting for measurement error when using measures constructed from high frequency data to conduct inference on dynamic jump models would appear to be advisable.
    Keywords: Dynamic price jumps, price jump tests, nonparametric jump measures, Hawkes process, discretized jump diffusion model, Bayesian Markov chain Monte Carlo.
    JEL: C12 C22 C58
    Date: 2017
  4. By: Michael Benzaquen; Jean-Philippe Bouchaud
    Abstract: We present an extended version of the recently proposed "LLOB" model for the dynamics of latent liquidity in financial markets. By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of latent order book. We compute in particular the finite memory corrections to the square root impact law, as well as the impact decay and the permanent impact of a meta-order. In addition, we consider the case of a spectrum of cancellation and deposition rates, which allows us to obtain a square root impact law for moderate participation rates, as observed empirically. Our multi-scale framework also provides an alternative solution to the so-called price diffusivity puzzle in the presence of a long-range correlated order flow.
    Date: 2017–10
  5. By: Hou, Yang; Li, Steven
    Abstract: The error correction coefficients, known as the loading factors, are a key component for price discovery measurement. To date, only constant loading factors have been considered for the price discovery measurement. This paper attempts to consider the autoregressive loading factors and their implications for the price discovery measurement. Based on the minute-by-minute data from the S&P 500 cash and E-mini futures markets, this paper reveals that the loading factors are indeed autoregressive. Furthermore, we propose three AR(1) processes for the loading factors and assess their performance in price discovery measurement compared to the constant loading factor model. Overall, this research provides supporting empirical evidence for using autoregressive loading factors for the price discovery measurement.
    Keywords: Price Discovery, Information Share, S&P 500 E-mini Futures, AGDCC GARCH, Loading Factor, Error Correction Coefficient
    JEL: G13 G14 G15
    Date: 2017–10–17

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