nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒08‒23
six papers chosen by
Thanos Verousis

  1. Liquidity Provision with Adverse Selection and Inventory Costs By Martin Herdegen; Johannes Muhle-Karbe; Florian Stebegg
  2. Simulation and estimation of an agent-based market-model with a matching engine By Ivan Jericevich; Patrick Chang; Tim Gebbie
  3. LOB modeling using Hawkes processes with a state-dependent factor By Emmanouil Sfendourakis; Ioane Muni Toke
  4. Fast Computation and Bandwidth Selection Algorithms for Smoothing Functional Time Series* By Bastian Schäfer; Yuanhua Feng
  5. Centralizing Over-the-Counter Markets? By Jason Allen; Milena Wittwer
  6. Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors By Klakow Akepanidtaworn; Rick Di Mascio; Alex Imas; Lawrence Schmidt

  1. By: Martin Herdegen; Johannes Muhle-Karbe; Florian Stebegg
    Abstract: We study one-shot Nash competition between an arbitrary number of identical dealers that compete for the order flow of a client. The client trades either because of proprietary information, exposure to idiosyncratic risk, or a mix of both trading motives. When quoting their price schedules, the dealers do not know the client's type but only its distribution, and in turn choose their price quotes to mitigate between adverse selection and inventory costs. Under essentially minimal conditions, we show that a unique symmetric Nash equilibrium exists and can be characterized by the solution of a nonlinear ODE.
    Date: 2021–07
  2. By: Ivan Jericevich; Patrick Chang; Tim Gebbie
    Abstract: An agent-based model with interacting low frequency liquidity takers inter-mediated by high-frequency liquidity providers acting collectively as market makers can be used to provide realistic simulated price impact curves. This is possible when agent-based model interactions occur asynchronously via order matching using a matching engine in event time to replace sequential calendar time market clearing. Here the matching engine infrastructure has been modified to provide a continuous feed of order confirmations and updates as message streams in order to conform more closely to live trading environments. The resulting trade and quote message data from the simulations are then aggregated, calibrated and visualised. Various stylised facts are presented along with event visualisations and price impact curves. We argue that additional realism in modelling can be achieved with a small set of agent parameters and simple interaction rules once interactions are reactive, asynchronous and in event time. We argue that the reactive nature of market agents may be a fundamental property of financial markets and when accounted for can allow for parsimonious modelling without recourse to additional sources of noise.
    Date: 2021–08
  3. By: Emmanouil Sfendourakis; Ioane Muni Toke
    Abstract: A point process model for order flows in limit order books is proposed, in which the conditional intensity is the product of a Hawkes component and a state-dependent factor. In the LOB context, state observations may include the observed imbalance or the observed spread. Full technical details for the computationally-efficient estimation of such a process are provided, using either direct likelihood maximization or EM-type estimation. Applications include models for bid and ask market orders, or for upwards and downwards price movements. Empirical results on multiple stocks traded in Euronext Paris underline the benefits of state-dependent formulations for LOB modeling, e.g. in terms of goodness-of-fit to financial data.
    Date: 2021–07
  4. By: Bastian Schäfer (Paderborn University); Yuanhua Feng (Paderborn University)
    Abstract: This paper examines data-driven estimation of the mean surface in nonparamet- ric regression for huge functional time series. In this framework, we consider the use of the double conditional smoothing (DCS), an equivalent but much faster translation of the 2D-kernel regression. An even faster, but again equivalent func- tional DCS (FCDS) scheme and a boundary correction method for the DCS/FCDS is proposed. The asymptotically optimal bandwidths are obtained and selected by an IPI (iterative plug-in) algorithm. We show that the IPI algorithm works well in practice in a simulation study and apply the proposals to estimate the spot-volatility and trading volume surface in high-frequency nancial data under a functional representation. Our proposals also apply to large lattice spatial or spatial-temporal data from any research area.
    Keywords: Spatial nonparametric regression, boundary correction, functional double conditional smoothing, bandwidth selection, spot volatility surface
    JEL: C14 C51
    Date: 2021–08
  5. By: Jason Allen; Milena Wittwer
    Abstract: In traditional over-the-counter (OTC) markets, investors trade bilaterally through intermediaries referred to as dealers. An important regulatory question is whether to centralize OTC markets by shifting trades onto centralized platforms. We address this question in the context of the liquid Canadian government bond market. We document that dealers charge markups even in this market and show that there is a price gap between large investors who have access to a centralized platform and small investors who do not. We specify a model to quantify how much of this price gap is due to platform access and assess welfare effects. The model predicts that not all investors would use the platform even if platform access were universal. Nevertheless, the price gap would close by 32%–47%. Welfare would increase by 9%–30% because more trades are conducted by dealers who have high values to trade.
    Keywords: Financial institutions; Market structure and pricing
    JEL: D40 D47 G10 G20 L10
    Date: 2021–08
  6. By: Klakow Akepanidtaworn; Rick Di Mascio; Alex Imas; Lawrence Schmidt
    Abstract: Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a unique dataset of institutional investors with portfolios averaging $573 million. A striking finding emerges: while there is clear evidence of skill in buying, selling decisions underperform substantially—even relative to random selling strategies. This holds despite the similarity between the two decisions in frequency, substance and consequences for performance. Evidence suggests that an asymmetric allocation of cognitive resources such as attention can explain the discrepancy: we document a systematic, costly heuristic process when selling but not when buying.
    JEL: D91 G02 G12 G4 G41
    Date: 2021–07

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