nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒03‒29
five papers chosen by
Thanos Verousis

  1. An estimation of cost-based market liquidity from daily high, low and close prices By Jawad Saleemi
  2. Electricity intraday price modeling with marked Hawkes processes By Thomas Deschatre; Pierre Gruet
  3. How did the asset markets change after the Global Financial Crisis? By Kuang-Liang Chang; Charles Ka Yui Leung
  4. Liquidity in the German corporate bond market: Has the CSPP made a difference? By Boneva, Lena; Islami, Mevlud; Schlepper, Kathi
  5. Universal Trading for Order Execution with Oracle Policy Distillation By Yuchen Fang; Kan Ren; Weiqing Liu; Dong Zhou; Weinan Zhang; Jiang Bian; Yong Yu; Tie-Yan Liu

  1. By: Jawad Saleemi (University of Gujrat, UPV - Universitat Politecnica de Valencia)
    Abstract: In the literature of asset pricing, this paper introduces a new method to estimate the cost-based market liquidity (CBML), that is, the bid-ask spread. The proposed model of spread proxy positively correlates with the examined low-frequency spread proxies for a larger dataset. The introduced approach provides potential implications in important aspects. Unlike in the Roll bid-ask spread model and the CHL bid-ask estimator, the CBML model consistently estimates market liquidity and trading cost for the entire dataset. Additionally, the CBML estimator steadily measures positive spreads, unlike in the CS bid-ask spread model. The construction of the proposed approach is not computationally intensive and can be considered for distinct studies at both market and firm levels.
    Abstract: Este documento presenta un nuevo método, en la literatura sobre fijación de precios de activos, para estimar la liquidez del mercado basada en el coste (CBML), es decir, el diferencial entre oferta y demanda. El modelo propuesto con un proxy del diferencial (spread) se correlaciona positivamente con los proxy del diferencial de baja frecuencia examinados para un conjunto de datos más grande. El enfoque introducido proporciona potenciales implicaciones en aspectos importantes. A diferencia del modelo de diferencial de oferta y demanda y el estimador CHL, el modelo CBML estima constantemente la liquidez del mercado y el costo comercial para todo el conjunto de datos. Además, el estimador CBML mide constantemente los diferenciales positivos, a diferencia del modelo de diferencial de oferta y demanda CS. La construcción del enfoque propuesto es asumible computacionalmente y puede considerarse para estudios distintos tanto a nivel de mercado como de empresa.
    Keywords: Market Liquidity,Bid-Ask Spread,Asset Pricing,Market Microstructure,Trading Cost,Microestructura del mercado,Fijación de precios de activos,Diferencial de oferta y demanda,Liquidez del mercado,Coste de negociación
    Date: 2020
  2. By: Thomas Deschatre; Pierre Gruet
    Abstract: We consider a 2-dimensional marked Hawkes process with increasing baseline intensity in order to model prices on electricity intraday markets. This model allows to represent different empirical facts such as increasing market activity, random jump sizes but above all microstructure noise through the signature plot. This last feature is of particular importance for practitioners and has not yet been modeled on those particular markets. We provide analytic formulas for first and second moments and for the signature plot, extending the classic results of Bacry et al. (2013) in the context of Hawkes processes with random jump sizes and time dependent baseline intensity. The tractable model we propose is estimated on German data and seems to fit the data well. We also provide a result about the convergence of the price process to a Brownian motion with increasing volatility at macroscopic scales, highlighting the Samuelson effect.
    Date: 2021–03
  3. By: Kuang-Liang Chang; Charles Ka Yui Leung
    Abstract: The Global Financial Crisis (GFC) changes the relative economic riskiness and risk-adjusted-performance of different asset markets. While the empirical distribution for stock return shifted to the right and became more concentrated around the mean after the GFC, the real estate market counterparts moved to the left and became more spread out. The economic risk of the OFHEO and Case-Shiller housing indices was smaller than the counterpart of the equity REIT (EREITs) market before the financial crisis, it substantially increased. Also, the economic performance of the OFHEO and Case-Shiller housing indices decreased after the financial crisis. They are below the performance indices of the stock and EREITs markets. The ex-post real estate premium vanishes. If we presume the "best model" to be the same before and after the GFC, we could severely misestimate the risk after the GFC.
    Date: 2021–03
  4. By: Boneva, Lena; Islami, Mevlud; Schlepper, Kathi
    Abstract: The Eurosystem purchased €178 billion of corporate bonds between June 2016 and December 2018 under the Corporate Sector Purchase Programme (CSPP). Did these purchases lead to a deterioration of liquidity conditions in the corporate bond market, thus raising concerns about unintended consequences of large-scale asset purchases? To answer this question, we combine the Bundesbank's detailed CSPP purchase records with a range of liquidity indicators for both purchased and nonpurchased bonds. We find that while the flow of purchases supported secondary market liquidity, liquidity conditions deteriorated in the long-run as the Bundesbank reduced the stock of corporate bonds available for trading in the secondary market.
    Keywords: Corporate Bond Market,Central Bank Asset Purchases,Market Liquidity
    JEL: E52 F30 G12
    Date: 2021
  5. By: Yuchen Fang; Kan Ren; Weiqing Liu; Dong Zhou; Weinan Zhang; Jiang Bian; Yong Yu; Tie-Yan Liu
    Abstract: As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.
    Date: 2021–01

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