nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒10‒01
four papers chosen by
Thanos Verousis

  1. State-dependent Hawkes processes and their application to limit order book modelling By Maxime Morariu-Patrichi; Mikko S. Pakkanen
  2. Lighting up the dark: Liquidity in the German corporate bond market By Gündüz, Yalin; Ottonello, Giorgio; Pelizzon, Loriana; Schneider, Michael; Subrahmanyam, Marti G.
  3. Deep Reinforcement Learning in High Frequency Trading By Prakhar Ganesh; Puneet Rakheja
  4. BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange By Dave Cliff

  1. By: Maxime Morariu-Patrichi; Mikko S. Pakkanen
    Abstract: We study statistical aspects of state-dependent Hawkes processes, which are an extension of Hawkes processes where a self- and cross-exciting counting process and a state process are fully coupled, interacting with each other. The excitation kernel of the counting process depends on the state process that, reciprocally, switches state when there is an event in the counting process. We first establish the existence and uniqueness of state-dependent Hawkes processes and explain how they can be simulated. Then we develop maximum likelihood estimation methodology for parametric specifications of the process. We apply state-dependent Hawkes processes to high-frequency limit order book data, allowing us to build a novel model that captures the feedback loop between the order flow and the shape of the limit order book. We estimate two specifications of the model, using the bid-ask spread and the queue imbalance as state variables, and find that excitation effects in the order flow are strongly state-dependent. Additionally, we find that the endogeneity of the order flow, measured by the magnitude of excitation, is also state-dependent, being more pronounced in disequilibrium states of the limit order book.
    Date: 2018–09
  2. By: Gündüz, Yalin; Ottonello, Giorgio; Pelizzon, Loriana; Schneider, Michael; Subrahmanyam, Marti G.
    Abstract: We study the impact of transparency on liquidity in OTC markets. We do so by providing an analysis of liquidity in a corporate bond market without trade transparency (Germany), and comparing our findings to a market with full post-trade disclosure (the U.S.). We employ a unique regulatory dataset of transactions of German financial institutions from 2008 until 2014 to find that: First, overall trading activity is much lower in the German market than in the U.S. Second, similar to the U.S., the determinants of German corporate bond liquidity are in line with search theories of OTC markets. Third, surprisingly, frequently traded German bonds have transaction costs that are 39-61 bp lower than a matched sample of bonds in the U.S. Our results support the notion that, while market liquidity is generally higher in transparent markets, a sub-set of bonds could be more liquid in more opaque markets because of investors "crowding" their demand into a small number of more actively traded securities.
    Keywords: Corporate Bonds,WpHG,Liquidity,Transparency,OTC markets
    JEL: G15
    Date: 2018
  3. By: Prakhar Ganesh; Puneet Rakheja
    Abstract: The ability to give a precise and fast prediction for the price movement of stocks is the key to profitability in High Frequency Trading. The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Reinforcement Learning and to argue why Deep RL can have a lot of potential in the field of High Frequency Trading. We have analyzed the model's performance based on it's prediction accuracy as well as prediction speed across full-day trading simulations.
    Date: 2018–09
  4. By: Dave Cliff
    Abstract: This paper describes the design, implementation, and successful use of the Bristol Stock Exchange (BSE), a novel minimal simulation of a centralised financial market, based on a Limit Order Book (LOB) such as is common in major stock exchanges. Construction of BSE was motivated by the fact that most of the world's major financial markets have automated, with trading activity that previously was the responsibility of human traders now being performed by high-speed autonomous automated trading systems. Research aimed at understanding the dynamics of this new style of financial market is hampered by the fact that no operational real-world exchange is ever likely to allow experimental probing of that market while it is open and running live, forcing researchers to work primarily from time-series of past trading data. Similarly, university-level education of the engineers who can create next-generation automated trading systems requires that they have hands-on learning experience in a sufficiently realistic teaching environment. BSE as described here addresses both those needs: it has been successfully used for teaching and research in a leading UK university since 2012, and the BSE program code is freely available as open-source on GitHuB.
    Date: 2018–09

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