nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒04‒03
three papers chosen by
Thanos Verousis

  1. The self-exciting nature of the bid-ask spread dynamics By Ruihua Ruan; Emmanuel Bacry; Jean-Fran\c{c}ois Muzy
  2. Volume dynamics around FOMC announcements By Sonya Zhu
  3. Realized recurrent conditional heteroskedasticity model for volatility modelling By Chen Liu; Chao Wang; Minh-Ngoc Tran; Robert Kohn

  1. By: Ruihua Ruan; Emmanuel Bacry; Jean-Fran\c{c}ois Muzy
    Abstract: The bid-ask spread, which is defined by the difference between the best selling price and the best buying price in a Limit Order Book at a given time, is a crucial factor in the analysis of financial securities. In this study, we propose a "State-dependent Spread Hawkes model" (SDSH) that accounts for various spread jump sizes and incorporates the impact of the current spread state on its intensity functions. We apply this model to the high-frequency data from the Cac40 Euronext market and capture several statistical properties, such as the spread distributions, inter-event time distributions, and spread autocorrelation functions.
    Date: 2023–03
  2. By: Sonya Zhu
    Abstract: The stock market volume decreases in anticipation of FOMC announcements and increases afterward. I develop a stylized model and attribute the volume dynamics to discretionary liquidity trading resulting from the presence of private information. Consistent with the model's prediction, I find information asymmetry increases ahead of FOMC announcements, especially before policy rate changes. Using firm-level high-frequency data, I also find, in the cross-section, that volume changes around these events are particularly stronger for stocks that are more exposed to discretionary liquidity trading. Volume dynamics and liquidity shocks can explain around one third of the pre-FOMC price drift.
    Keywords: macroeconomic news, trading volume, liquidity, information asymmetry
    JEL: D18 G12 G14
    Date: 2023–03
  3. By: Chen Liu; Chao Wang; Minh-Ngoc Tran; Robert Kohn
    Abstract: We propose a new approach to volatility modelling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distills modeling advances from financial econometrics, high frequency trading data and deep learning. Bayesian inference via the Sequential Monte Carlo method is employed for statistical inference and forecasting. The new framework can jointly model the returns and realized volatility measures, has an excellent in-sample fit and superior predictive performance compared to several benchmark models, while being able to adapt well to the stylized facts in volatility. The performance of the new framework is tested using a wide range of metrics, from marginal likelihood, volatility forecasting, to tail risk forecasting and option pricing. We report on a comprehensive empirical study using 31 widely traded stock indices over a time period that includes COVID-19 pandemic.
    Date: 2023–02

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