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on Market Microstructure |
| By: | Eleni Gousgounis; Scott Mixon; Tugkan Tuzun; Clara Vega |
| Abstract: | We study the behavior of liquidity providers and liquidity consumers in the 10-year U.S. Treasury futures market during the height of the COVID-19 shock in March 2020, a period of market turmoil when demand for liquidity was high. In March 2020, PTFs reduced their volume of liquidity providing trades as a share of total trading volume. However, they still accounted for the lion share of total liquidity provision and their liquidity provision improved market liquidity. In contrast, dealers (banks and non-banks) increased their volume of liquidity providing trades as a share of total trading volume, but their activity did not have a large effect on overall liquidity. Among the traders that place liquidity consuming trades, asset managers had the largest impact on liquidity by increasing transaction costs. Despite a significant attention to the role of basis traders in the Treasury market disruption of March 2020, we do not find evidence for basis traders being important drivers of disruption in Treasury futures market. |
| Keywords: | PTFs; Basis traders; Treasury futures |
| JEL: | G10 G13 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-38 |
| By: | Comerton-Forde, Carole (University of Melbourne); Ford, Billy (None); Foucault, Thierry (HEC Paris); Jurkatis, Simon (Bank of England) |
| Abstract: | Investors act as a liquidity backstop in the corporate bond market. By providing liquidity, investors help ease dealers’ balance sheet constraints, especially during market stress. During the March 2020 Dash-for-Cash, in bonds where investors stopped providing liquidity, transaction costs rose by 38%. We find the composition of types of liquidity providers – rather than just their presence – shapes trading costs. Dealers relying on flexible-mandate investors, such as hedge funds, are more resilient to liquidity shocks. Dealers offer discounts to investors for past liquidity services to maintain liquidity provider networks. These discounts represent two thirds of relationship discounts. |
| Keywords: | Bond markets; liquidity; client-sourced liquidity; balance sheet cost |
| JEL: | G10 G14 G23 |
| Date: | 2025–05–16 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1126 |
| By: | Emmanouil Sfendourakis |
| Abstract: | We present a Markovian market model driven by a hidden Brownian efficient price. In particular, we extend the queue-reactive model, making its dynamics dependent on the efficient price. Our study focuses on two sub-models: a signal-driven price model where the mid-price jump rates depend on the efficient price and an observable signal, and the usual queue-reactive model dependent on the efficient price via the intensities of the order arrivals. This way, we are able to correlate the evolution of limit order books of different stocks. We prove the stability of the observed mid-price around the efficient price under natural assumptions. Precisely, we show that at the macroscopic scale, prices behave as diffusions. We also develop a maximum likelihood estimation procedure for the model, and test it numerically. Our model is them used to backest trading strategies in a liquidation context. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.11843 |
| By: | Wenhao Guo; Yuda Wang; Zeqiao Huang; Changjiang Zhang; Shumin ma |
| Abstract: | In the complex landscape of traditional futures trading, where vast data and variables like real-time Limit Order Books (LOB) complicate price predictions, we introduce the FutureQuant Transformer model, leveraging attention mechanisms to navigate these challenges. Unlike conventional models focused on point predictions, the FutureQuant model excels in forecasting the range and volatility of future prices, thus offering richer insights for trading strategies. Its ability to parse and learn from intricate market patterns allows for enhanced decision-making, significantly improving risk management and achieving a notable average gain of 0.1193% per 30-minute trade over state-of-the-art models with a simple algorithm using factors such as RSI, ATR, and Bollinger Bands. This innovation marks a substantial leap forward in predictive analytics within the volatile domain of futures trading. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.05595 |
| By: | Mihai Cucuringu; Kang Li; Chao Zhang |
| Abstract: | This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the current scarcity of flexible methods in this area, we employ a suite of machine learning (ML) models enriched with numerous HF predictors to enhance the predictability of intraday trading volumes. Our findings reveal that intraday stock trading volume is highly predictable, especially with ML and considering commonality. Additionally, we assess the economic benefits of accurate volume forecasting through Volume Weighted Average Price (VWAP) strategies. The results demonstrate that precise intraday forecasting offers substantial advantages, providing valuable insights for traders to optimize their strategies. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08180 |
| By: | Philippe Bergault; Pierre Cardaliaguet; Wenbin Yan |
| Abstract: | This paper investigates the optimal hedging strategies of an informed broker interacting with multiple traders in a financial market. We develop a theoretical framework in which the broker, possessing exclusive information about the drift of the asset's price, engages with traders whose trading activities impact the market price. Using a mean-field game approach, we derive the equilibrium strategies for both the broker and the traders, illustrating the intricate dynamics of their interactions. The broker's optimal strategy involves a Stackelberg equilibrium, where the broker leads and the traders follow. Our analysis also addresses the mean field limit of finite-player models and shows the convergence to the mean-field solution as the number of traders becomes large. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.08992 |