nep-mst New Economics Papers
on Market Microstructure
Issue of 2014‒09‒29
five papers chosen by
Thanos Verousis

  1. On the design of sell-side limit and market order tactics By Vladimir Markov
  2. Forecasting Realized Volatility Using Subsample Averaging By Tae-Hwy Lee; Huiyu Huang
  3. Optimal Execution with Dynamic Order Flow Imbalance By Kyle Bechler; Mike Ludkovski
  4. A stochastic control approach for options market making By Sofiene El Aoud; Frédéric Abergel
  5. Heterogeneous Fundamentalists and Market Maker Inventories By Alessandro Carraro; Giorgio Ricchiuti

  1. By: Vladimir Markov
    Abstract: This article provides a novel framework to evaluate limit order tactics that highlights expected fill price, adverse price selection cost, and opportunity cost. We formulate the problem of optimal execution of market orders with nonlinear market impact, power law decay kernel, and stochastic and deterministic liquidity constraints. We demonstrate how these tactics can be incorporated in the uncertainty bands framework.
    Date: 2014–09
  2. By: Tae-Hwy Lee (Department of Economics, University of California Riverside); Huiyu Huang (Grantham, Mayo, Van Otterloo and Company LLC)
    Abstract: When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While the subsample-averaging has been proposed and used in estimating RV, this paper is the first that uses the subsample-averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, that generates forecasts from each subsample, and then combine these forecasts. We find that, in daily S&P 500 return RV forecasts, subsample-averaging generates better forecasts than those using only one subsample without averaging over all subsamples.
    Keywords: Subsample averaging. Forecast combination. High-frequency data. Realized volatility. ARFIMA model. HAR model.
    JEL: C53 C58 G17
    Date: 2014–09
  3. By: Kyle Bechler; Mike Ludkovski
    Abstract: We examine optimal execution models that take into account both market microstructure impact and informational costs. Informational footprint is related to order flow and is represented by the trader's influence on the flow imbalance process, while microstructure influence is captured by instantaneous price impact. We propose a continuous-time stochastic control problem that balances between these two costs. Incorporating order flow imbalance leads to the consideration of the current market state and specifically whether one's orders lean with or against the prevailing order flow, key components often ignored by execution models in the literature. In particular, to react to changing order flow, we endogenize the trading horizon $T$. After developing the general indefinite-horizon formulation, we investigate several tractable approximations that sequentially optimize over price impact and over $T$. These approximations, especially a dynamic version based on receding horizon control, are shown to be very accurate and connect to the prevailing Almgren-Chriss framework. We also discuss features of empirical order flow and links between our model and "Optimal Execution Horizon" by Easley et al (Mathematical Finance, 2013).
    Date: 2014–09
  4. By: Sofiene El Aoud (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris, FiQuant - Chaire de finance quantitative - Ecole Centrale Paris); Frédéric Abergel (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris, FiQuant - Chaire de finance quantitative - Ecole Centrale Paris)
    Abstract: In this paper, we establish a model for market making in options whose underlying is perfectly liquid. In our model framework, the stock price follows a generic stochastic volatility model under the real-world probability measure P. Market participants price options on this stock under a risk-neutral pricing measure Q, and they may misspecify the parameters controlling the dynamics of the volatility process. We consider that there is an agent who is willing to make markets in an option on the stock with the aim of maximizing his expected utility from terminal wealth at the maturity of this option. Since market impact is an important feature in the microscopic time scale and should be taken into account in high frequency trading, we study di erent forms of this function argued in the recent literature. Through the use of optimal stochastic control, we provide exact expressions of optimal bid and ask quotes of the market making strategy in the case where the agent is risk-neutral. Afterward, we suppose that the agent is risk-averse and wants to reduce the variance of the nal wealth. In addition, this agent tries not to accumulate a large inventory in order not to have a signi cant exposure to market risk. For this purpose, we perturb the utility function by a penalty on the variance of nal wealth and also on accumulated inventory. Using singular perturbation with respect to the penalty parameter, we provide analytic approximations of the optimal bid and ask quotes. In order to con rm our theoretical results, we perform Monte Carlo simulations of the optimal market making strategy in the case where the stock price process follows a Heston model. We show that the opti- mal strategy is more pro table than a zero-intelligence strategy. Besides, we highlight the e ects of the misspeci cation of the parameters on the performance of the strategy.
    Date: 2014–07–01
  5. By: Alessandro Carraro (Dipartimento di Scienze per l'Economia e l'Impresa); Giorgio Ricchiuti (Dipartimento di Scienze per l'Economia e l'Impresa)
    Abstract: In this paper we develop an heterogenous agents model of asset price and inventory with a market maker who considers the excess demand of two groups of agents that employ the same trading rule (i.e. fundamentalists) with different beliefs on the fundamental value. The dynamics of our model is driven by a bi-dimensional discrete non-linear map. We show that the market maker has a destabilizing role when she actively manages the inventory. Moreover, inventory share and the distance between agents' beliefs strongly influence the results: market instability and periodic, or even, chaotic price fluctuations can be generated. Finally, we show through simulations that endogenous fluctuations of the fractions of agents may trigger to instability for a larger set of the parameters.
    Keywords: heterogeneous agents models; market maker inventory; chaos;
    JEL: C61 C63 D84 G12
    Date: 2014

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