nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒09‒10
five papers chosen by
Thanos Verousis

  1. The microstructure of high frequency markets By Rene Carmona; Kevin Webster
  2. Tensor Representation in High-Frequency Financial Data for Price Change Prediction By Dat Tran Thanh; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  3. Smooth Solutions to Portfolio Liquidation Problems under Price-Sensitive Market Impact * By Paulwin Graewe; Ulrich Horst; Eric Séré
  4. A Scaling Limit for Limit Order Books Driven by Hawkes Processes By Ulrich Horst; Wei Xu
  5. Effects of capital controls on foreign exchange liquidity By Carlos Cantú

  1. By: Rene Carmona; Kevin Webster
    Abstract: We present a novel approach to describing the microstructure of high frequency trading using two key elements. First we introduce a new notion of informed trader which we starkly contrast to current informed trader models. We describe the exact nature of the `superior information' high frequency traders have access to, and how these agents differ from the more standard `insider traders' described in past papers. This then leads to a model and an empirical analysis of the data which strongly supports our claims. The second key element is a rigorous description of clearing conditions on a limit order book and how to derive correct formulas for such a market. From a theoretical point of view, this allows the exact identification of two frictions in the market, one of which is intimately linked to our notion of `superior information'. Empirically, we show that ignoring these frictions can misrepresent the wealth exchanged on the market by 50%. Finally, we showcase two applications of our approach: we measure the profits made by high frequency traders on NASDAQ and re-visit the standard Black - Scholes model to determine how trading frictions alter the delta-hedging strategy.
    Date: 2017–09
  2. By: Dat Tran Thanh; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.
    Date: 2017–09
  3. By: Paulwin Graewe (Department of Mathematics - Humboldt Universität zu Berlin [Berlin]); Ulrich Horst (Department of Mathematics - Humboldt Universität zu Berlin [Berlin]); Eric Séré (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris-Dauphine - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We consider the stochastic control problem of a financial trader that needs to unwind a large asset portfolio within a short period of time. The trader can simultaneously submit active orders to a primary market and passive orders to a dark pool. Our framework is flexible enough to allow for price-dependent impact functions describing the trading costs in the primary market and price-dependent adverse selection costs associated with dark pool trading. We prove that the value function can be characterized in terms of the unique smooth solution to a PDE with singular terminal value, establish its explicit asymptotic behavior at the terminal time, and give the optimal trading strategy in feedback form.
    Keywords: portfolio liquidation,stochastic optimal control,singular terminal value
    Date: 2017
  4. By: Ulrich Horst; Wei Xu
    Abstract: In this paper we derive a scaling limit for an infinite dimensional limit order book model driven by Hawkes random measures. The dynamics of the incoming order flow is allowed to depend on the current market price as well as on a volume indicator. With our choice of scaling the dynamics converges to a coupled SDE-ODE system where limiting best bid and ask price processes follows a diffusion dynamics, the limiting volume density functions follows an ODE in a Hilbert space and the limiting order arrival and cancellation intensities follow a Volterra-Fredholm integral equation.
    Date: 2017–09
  5. By: Carlos Cantú
    Abstract: The literature on capital controls has focused on their use as tools to manage capital and improve macroeconomic and financial stability. However, there is a lack of analysis of their effect on foreign exchange (FX) market liquidity. In particular, technological and regulatory changes in FX markets over the past decade have had an influence on the effect of capital controls on alternative indicators of FX liquidity. In this paper, we introduce a theoretical model showing that, if capital controls are modelled as entry costs, then fewer investors will enter an economy. This will reduce the market's ability to accommodate large order flows without a significant change in the exchange rate (a market depth measure of liquidity). On the other hand, if capital controls are modelled as transaction costs, they can reduce the effective spread (a cost-based measure of liquidity). Using a panel of 20 emerging market economies and a novel measure of capital account restrictiveness, we provide empirical evidence showing that capital controls can reduce cost-based measures of FX market liquidity. The results imply that capital controls are effective in reducing the implicit cost component of FX market liquidity but can also have a negative structural effect on the FX market by making it more vulnerable to order flow imbalances.
    Keywords: capital flow management policies, foreign exchange market, market liquidity, market depth
    JEL: F31 G11 G15
    Date: 2017–08

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