nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒08‒14
six papers chosen by
Thanos Verousis

  1. Tackling the Problem of State Dependent Execution Probability: Empirical Evidence and Order Placement By Timoth\'ee Fabre; Vincent Ragel
  2. Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data By Ruijun Bu; Degui Li; Oliver Linton; Hanchao Wang
  3. Do spot market auction data help price discovery? By Adrian Fernandez-Perez; Joëlle Miffre; Tilman Schoen; Ayesha Scott
  4. Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods By Ioanna-Yvonni Tsaknaki; Fabrizio Lillo; Piero Mazzarisi
  5. A simple microstructure model based on the Cox-BESQ process with application to optimal execution policy By José da Fonseca; Yannick Malevergne
  6. Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications By Deniz Erdemlioglu; Christopher J. Neely; Xiye Yang

  1. By: Timoth\'ee Fabre; Vincent Ragel
    Abstract: Order placement tactics play a crucial role in high-frequency trading algorithms and their design is based on understanding the dynamics of the order book. Using high quality high-frequency data and survival analysis, we exhibit strong state dependence properties of the fill probability function. We define a set of microstructure features and train a multi-layer perceptron to infer the fill probability function. A weighting method is applied to the loss function such that the model learns from censored data. By comparing numerical results obtained on both digital asset centralized exchanges (CEXs) and stock markets, we are able to analyze dissimilarities between the fill probability of small tick crypto pairs and large tick assets -- large, relative to cryptos. The practical use of this model is illustrated with a fixed time horizon execution problem in which both the decision to post a limit order or to immediately execute and the optimal distance of placement are characterized. We discuss the importance of accurately estimating the clean-up cost that occurs in the case of a non-execution and we show it can be well approximated by a smooth function of market features. We finally assess the performance of our model with a backtesting approach that avoids the insertion of hypothetical orders and makes possible to test the order placement algorithm with orders that realistically impact the price formation process.
    Date: 2023–07
  2. By: Ruijun Bu; Degui Li; Oliver Linton; Hanchao Wang
    Abstract: In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one. For the high-frequency data contaminated by microstructure noise, we introduce a localised pre-averaging estimation method that reduces the effective magnitude of the noise. We then use the estimation tool developed in the noise-free scenario, and derive the uniform convergence rates for the developed spot volatility matrix estimator. We further combine the kernel smoothing with the shrinkage technique to estimate the time-varying volatility matrix of the high-dimensional noise vector. In addition, we consider large spot volatility matrix estimation in time-varying factor models with observable risk factors and derive the uniform convergence property. We provide numerical studies including simulation and empirical application to examine the performance of the proposed estimation methods in finite samples.
    Date: 2023–07
  3. By: Adrian Fernandez-Perez (AUT - Auckland University of Technology); Joëlle Miffre (Audencia Business School); Tilman Schoen (Mars Food); Ayesha Scott (AUT - Auckland University of Technology)
    Abstract: This paper contributes to the price discovery literature by establishing, for the first time, the role of commodity spot market auction data. Using the New Zealand whole milk powder market as an example, we show that auction-level data explain the price discovery dynamics above and beyond determinants previously identified as being relevant to spot and futures market price formation. In particular, the price discovery of the futures market rises with the volume of dairy products traded at the auction, signaling that the volume auctioned induces a change in the trading strategies of futures market participants. The whole milk powder discovery process is found to primarily take place in the spot market, which aligns well with the auction predating the introduction of the futures market, its higher volume, and lower trading costs.
    Keywords: Price discovery, Auction data, Dairy products, Spot and futures markets
    Date: 2023–09–01
  4. By: Ioanna-Yvonni Tsaknaki; Fabrizio Lillo; Piero Mazzarisi
    Abstract: Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.
    Date: 2023–07
  5. By: José da Fonseca (AUT - Auckland University of Technology, UP1 - Université Paris 1 Panthéon-Sorbonne, PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne); Yannick Malevergne (UP1 - Université Paris 1 Panthéon-Sorbonne, PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne)
    Abstract: We develop a microstructure model whose order flow is driven by a Cox-BESQ process. We derive important analytical properties of the Cox-BESQ process in order to explicit the stock price dynamics at different time scales, provide different parameter estimators and solve the optimal execution problem. We implement the model using a large data set of stock index and bond futures. Our results show that the Cox-BESQ process provides an alternative framework to the Hawkes process to build a microstructure model that is very flexible and has an explicit solution.
    Keywords: Microstructure model, Stochastic intensity model, Cox-BESQ process, Optimal execution
    Date: 2021–07
  6. By: Deniz Erdemlioglu; Christopher J. Neely; Xiye Yang
    Abstract: We develop a new framework to measure market-wide (systemic) tail risk in the cross-section of high-frequency stock returns. We estimate the time-varying jump intensities of asset prices and introduce a testing approach that identifies multi-asset tail risk based on the release times of scheduled news announcements. Using high-frequency data on individual U.S. stocks and sector-specific ETF portfolios, we find that most of the FOMC announcements create systemic left tail risk, but there is no evidence that macro announcements do so. The magnitude of the tail risk induced by Fed news varies over the business cycle, peaks during the global financial crisis and remains high over different phases of unconventional monetary policy. We use our approach to construct a Fed-induced systemic tail risk (STR) indicator. STR helps explain the pre-FOMC announcement drift and significantly increases variance risk premia, particularly for the meetings without press conferences.
    Keywords: time-varying tail risk; high-frequency data; Federal Open Market Committee (FOMC) news; monetary policy announcements; cojumps; systemic risk; jump intensity
    JEL: C12 C14 C22 C32 C58 G12 G14
    Date: 2023–07–20

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