nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒05‒11
five papers chosen by
Thanos Verousis

  1. Price Discovery and Liquidity Recovery: Forex Market Reactions to Macro Announcements By Masahiro Yamada; Takatoshi Ito
  2. A Stochastic LQR Model for Child Order Placement in Algorithmic Trading By Jackie Jianhong Shen
  3. Dynamical regularities of US equities opening and closing auctions By Damien Challet; Nikita Gourianov
  4. Statistically validated leadlag networks and inventory prediction in the foreign exchange market By Damien Challet; Rémy Chicheportiche; Mehdi Lallouache; Serge Kassibrakis
  5. Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories By Micha{\l} Narajewski; Florian Ziel

  1. By: Masahiro Yamada; Takatoshi Ito
    Abstract: We examine whether the forex market quality, measured by the speed of price discovery and liquidity recovery after macro statistics announcements, has improved using the EBS high-frequency data for 20 years. Considering the recent rise of computer-based trading, a popular conjecture is that the market quality has improved. Our empirical analysis, however, suggests that an improving trend is only observed in price discovery. Moreover, two measures are negatively correlated because an increasing number of traders improves liquidity but slows down price discovery. Theoretically, the latter finding implies that “fast” traders have a poor interpretation of how the news will impact prices.
    JEL: E44 F31 G14 G15
    Date: 2020–04
  2. By: Jackie Jianhong Shen
    Abstract: Modern Algorithmic Trading ("Algo") allows institutional investors and traders to liquidate or establish big security positions in a fully automated or low-touch manner. Most existing academic or industrial Algos focus on how to "slice" a big parent order into smaller child orders over a given time horizon. Few models rigorously tackle the actual placement of these child orders. Instead, placement is mostly done with a combination of empirical signals and heuristic decision processes. A self-contained, realistic, and fully functional Child Order Placement (COP) model may never exist due to all the inherent complexities, e.g., fragmentation due to multiple venues, dynamics of limit order books, lit vs. dark liquidity, different trading sessions and rules. In this paper, we propose a reductionism COP model that focuses exclusively on the interplay between placing passive limit orders and sniping using aggressive takeout orders. The dynamic programming model assumes the form of a stochastic linear-quadratic regulator (LQR) and allows closed-form solutions under the backward Bellman equations. Explored in detail are model assumptions and general settings, the choice of state and control variables and the cost functions, and the derivation of the closed-form solutions.
    Date: 2020–04
  3. By: Damien Challet (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Nikita Gourianov (Department of Physics [Oxford] - University of Oxford [Oxford])
    Abstract: We first investigate static properties of opening and closing auctions such as typical auction volume relative to daily volume and order value distributions. We then show that the indicative match price is strongly mean-reverting because the imbalance is, which we link to strategic behavior. Finally, we investigate how the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book.
    Keywords: Auctions,US equities,Linear response,Imbalance,Liquidity
    Date: 2019
  4. By: Damien Challet (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Rémy Chicheportiche (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Mehdi Lallouache (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Serge Kassibrakis
    Abstract: We introduce a method to infer lead-lag networks of agents' actions in complex systems. These networks open the way to both microscopic and macroscopic states prediction in such systems. We apply this method to trader-resolved data in the foreign exchange market. We show that these networks are remarkably persistent, which explains why and how order flow prediction is possible from trader-resolved data. In addition, if traders' actions depend on past prices, the evolution of the average price paid by traders may also be predictable. Using random forests, we verify that the predictability of both the sign of order flow and the direction of average transaction price is strong for retail investors at an hourly time scale, which is of great relevance to brokers and order matching engines. Finally, we argue that the existence of trader lead-lag networks explains in a self-referential way why a given trader becomes active, which is in line with the fact that most trading activity has an endogenous origin.
    Keywords: lead-lag networks,trader-resolved data,foreign exchange,prediction,inventory management
    Date: 2018–12–03
  5. By: Micha{\l} Narajewski; Florian Ziel
    Abstract: Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a mixture of the Dirac and the Student's t-distributions. Moreover, the mixing term is estimated using a high-dimensional logistic regression with lasso penalty. We model the expected value and volatility of the series using i.a. autoregressive and no-trade effects or load, wind and solar generation forecasts and accounting for the non-linearities in e.g. time to maturity. Both the in-sample characteristics and forecasting performance are analysed using a rolling window forecasting study. Multiple versions of the model are compared to several benchmark models. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets. The results prove superiority of the mixture model over the benchmarks gaining the most from the modelling of the volatility.
    Date: 2020–05

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