Paper: arXiv 2405.18594

Abstract

In this article, we delve into the applications and extensions of the queue-reactive model for the simulation of limit order books. Our approach emphasizes the importance of order sizes, in conjunction with their type and arrival rate, by integrating the current state of the order book to determine, not only the intensity of order arrivals and their type, but also their sizes. These extensions generate simulated markets that are in line with numerous stylized facts of the market. Our empirical calibration, using futures on German bonds, reveals that the extended queue-reactive model significantly improves the description of order flow properties and the shape of queue distributions. Moreover, our findings demonstrate that the extended model produces simulated markets with a volatility comparable to historical real data, utilizing only endogenous information from the limit order book. This research underscores the potential of the queue-reactive model and its extensions in accurately simulating market dynamics and providing valuable insights into the complex nature of limit order book modeling.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced stochastic processes (Hawkes-like intensity functions) and maximum likelihood estimation for model calibration, indicating high mathematical complexity. It also demonstrates strong empirical rigor through calibration on real futures data, comparison with stylized facts, and validation of simulated volatility against historical data.

Research Flowchart

  flowchart TD
  A["Research Goal: Extend Queue-Reactive Model<br>to incorporate order sizes"] --> B["Methodology: Integrate Order Size Dynamics<br>with Queue State & Type/Arrival Rates"]
  B --> C["Data Input: High-Frequency Limit Order Book Data<br>(Futures on German Bonds)"]
  C --> D["Computational Process:<br>Empirical Calibration & Simulation"]
  D --> E["Key Findings:<br>1. Improved Order Flow Description<br>2. Accurate Queue Distributions<br>3. Volatility Matching (Endogenous)"]

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2 of 52 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.