nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒12‒12
three papers chosen by
Thanos Verousis

  1. DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift By Defu Cao; Yousef El-Laham; Loc Trinh; Svitlana Vyetrenko; Yan Liu
  2. An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations By Vladim\'ir Hol\'y
  3. Emissions trading with transaction costs By Baudry, Marc; Faure, Anouk; Quemin, Simon

  1. By: Defu Cao; Yousef El-Laham; Loc Trinh; Svitlana Vyetrenko; Yan Liu
    Abstract: In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen scenarios. In this work, we utilize a multi-agent market simulator to build a synthetic LOB dataset, named DSLOB, with and without market stress scenarios, which allows for the design of controlled distributional shift benchmarking. Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods. Our results reflect the need for increased researcher efforts to develop algorithms with robustness to distributional shifts in high-frequency time series data.
    Date: 2022–11
  2. By: Vladim\'ir Hol\'y
    Abstract: We develop a novel observation-driven model for high-frequency prices. We account for irregularly spaced observations, simultaneous transactions, discreteness of prices, and market microstructure noise. The relation between trade durations and price volatility, as well as intraday patterns of trade durations and price volatility, is captured using smoothing splines. The dynamic model is based on the zero-inflated Skellam distribution with time-varying volatility in a score-driven framework. Market microstructure noise if filtered by including a moving average component. The model is estimated by the maximum likelihood method. In an empirical study of the IBM stock, we demonstrate that the model provides a good fit to the data. Besides modeling intraday volatility, it can also be used to measure daily realized volatility.
    Date: 2022–11
  3. By: Baudry, Marc; Faure, Anouk; Quemin, Simon
    Abstract: We develop an equilibrium model of emissions permit trading in the presence of fixed and proportional trading costs in which the permit price and firms' participation in and extent of trading are endogenously determined. We analyze the sensitivity of the equilibrium to changes in the trading costs and firms' allocations, and characterize situations where the trading costs depress or raise permit prices relative to frictionless market conditions. We calibrate our model to annual transaction data in Phase II of the EU ETS (2008–2012) and find that trading costs in the order of 10 k€ per annum plus 1 € per permit traded substantially reduce discrepancies between observations and theoretical predictions for firms’ behavior (e.g. autarkic compliance for small and/or long firms). Our simulations suggest that ignoring trading costs leads to an underestimation of the price impacts of supply-curbing policies, this difference varying with the incidence on firms.
    Keywords: emissions trading; EU ETS; policy design and evaluation; transaction costs; Grantham Foundation for the Protection of the Environment ; the UK Economic and Social Research Council
    JEL: D23 H32 L22 Q52 Q58
    Date: 2021–07–01

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