nep-mst New Economics Papers
on Market Microstructure
Issue of 2015‒08‒25
five papers chosen by
Thanos Verousis

  1. Rock around the clock: an agent-based model of low- and high-frequency trading By Sandrine Jacob Leal; Mauro Napoletano; Andrea Roventini; Giorgio Fagiolo
  2. Liquidity Supply across Multiple Trading Venues By Laurence Lescourret; Sophie Moinas
  3. An optimal trading problem in intraday electricity markets * By René Aïd; Pierre Gruet; Huyên Pham
  5. Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval By Nina Munkholt Jakobsen; Michael Sørensen

  1. By: Sandrine Jacob Leal (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis - CNRS); Mauro Napoletano (OFCE - OFCE - Sciences Po); Andrea Roventini (Department of Economics - Tilburg University); Giorgio Fagiolo (LEM - Laboratory of Economics and Management - Sant'Anna School of Advanced Studies)
    Abstract: We build an agent-based model to study how the interplay between low- and high frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. On the contrary, high-frequency traders activation is event-driven and depends on price the contrary, high-frequency traders activation is event-driven and depends on price formation produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we found that the presence of high-frequency trading increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of high-frequency traders, i.e., their ability to i) generate high bid-ask spreads and ii) synchronize on the sell side of the limit order book. Finally, we found that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.
    Date: 2014–02
  2. By: Laurence Lescourret (Finance Department - Essec Business School); Sophie Moinas (Finance - CRM - Centre de Recherche Management - CNRS - UT1 - Université Toulouse 1 Capitole)
    Abstract: Financial markets are increasingly fragmented. How to supply liquidity in this environment? Using an inventory model, we analyze how two strategic intermediaries compete across two venues that can be hit simultaneously by liquidity shocks of equal or opposite signs. Although order flow is fragmented ex-ante, we show that intermediaries might strategically consolidate it ex-post, improving global liquidity. We also find that local spreads co-move together across venues as a result of global inventory management. Using Euronext proprietary data, we uncover new evidence of inventory control across venues and find that local spreads vary in a way uniquely predicted by the model.
    Date: 2015–03–15
  3. By: René Aïd (FiME Lab - Laboratoire de Finance des Marchés d'Energie - Université Paris IX - Paris Dauphine - CREST - EDF R&D); Pierre Gruet (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS - UP7 - Université Paris Diderot - Paris 7 - UPMC - Université Pierre et Marie Curie - Paris 6); Huyên Pham (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS - UP7 - Université Paris Diderot - Paris 7 - UPMC - Université Pierre et Marie Curie - Paris 6, ENSAE Paris-Tech & CREST, Laboratoire de Finance et d'Assurance - ENSAE Paris-Tech & CREST)
    Abstract: We consider the problem of optimal trading for a power producer in the context of intraday electricity markets. The aim is to minimize the imbalance cost induced by the random residual demand in electricity, i.e. the consumption from the clients minus the production from renewable energy. For a simple linear price impact model and a quadratic criterion, we explicitly obtain approximate optimal strategies in the intraday market and thermal power generation, and exhibit some remarkable properties of the trading rate. Furthermore, we study the case when there are jumps on the demand forecast and on the intraday price, typically due to error in the prediction of wind power generation. Finally, we solve the problem when taking into account delay constraints in thermal power production.
    Date: 2015–01–19
  4. By: Hacène Djellout (Laboratoire de Mathématiques - UBP - Université Blaise Pascal - Clermont-Ferrand 2 - CNRS); Hui Jiang (Nanjing University of Aeronautics and Astronautics - Department of Mathematics)
    Abstract: Recently a considerable interest has been paid on the estimation problem of the realized volatility and covolatility by using high-frequency data of financial price processes in financial econometrics. Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this paper, we adopt the threshold estimator introduced by Mancini where only the variations under a given threshold function are taken into account. The purpose of this work is to investigate large and moderate deviations for the threshold estimator of the integrated variance-covariance vector. This paper is an extension of the previous work in Djellout Guillin and Samoura where the problem has been studied in absence of the jump component. We will use the approximation lemma to prove the LDP. As the reader can expect we obtain the same results as in the case without jump.
    Date: 2015–04–03
  5. By: Nina Munkholt Jakobsen (University of Copenhagen); Michael Sørensen (University of Copenhagen and CREATES)
    Abstract: Parametric estimation for diffusion processes is considered for high frequency observations over a fixed time interval. The processes solve stochastic differential equations with an unknown parameter in the diffusion coefficient. We find easily verified conditions on approximate martingale estimating functions under which estimators are consistent, rate optimal, and efficient under high frequency (in-fill) asymptotics. The asymptotic distributions of the estimators are shown to be normal variance-mixtures, where the mixing distribution generally depends on the full sample path of the diffusion process over the observation time interval. Utilising the concept of stable convergence, we also obtain the more easily applicable result that for a suitable data dependent normalisation, the estimators converge in distribution to a standard normal distribution. The theory is illustrated by a small simulation study comparing an efficient and a non-efficient estimating function.
    Keywords: Approximate martingale estimating functions, discrete time sampling of diffusions, in-fill asymptotics, normal variance-mixtures, optimal rate, random Fisher information, stable convergence, stochastic differential equation.
    JEL: C22
    Date: 2015–08–06

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