Discrete $q$-exponential limit order cancellation time distribution

ArXiv ID: 2306.00093 “View on arXiv”

Authors: Unknown

Abstract

Modeling financial markets based on empirical data poses challenges in selecting the most appropriate models. Despite the abundance of empirical data available, researchers often face difficulties in identifying the best-fitting model. Long-range memory and self-similarity estimators, commonly used for this purpose, can yield inconsistent parameter values, as they are tailored to specific time series models. In our previous work, we explored order disbalance time series from the broader perspective of fractional L’{“e”}vy stable motion, revealing a stable anti-correlation in the financial market order flow. However, a more detailed analysis of empirical data indicates the need for a more specific order flow model that incorporates the power-law distribution of limit order cancellation times. When considering a series in event time, the limit order cancellation times follow a discrete probability mass function derived from the Tsallis q-exponential distribution. The combination of power-law distributions for limit order volumes and cancellation times introduces a novel approach to modeling order disbalance in the financial markets. Moreover, this proposed model has the potential to serve as an example for modeling opinion dynamics in social systems. By tailoring the model to incorporate the unique statistical properties of financial market data, we can improve the accuracy of our predictions and gain deeper insights into the dynamics of these complex systems.

Keywords: Order Flow Modeling, Fractional Lévy Stable Motion, Power-law Distributions, Market Microstructure, Tsallis q-exponential

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper uses advanced stochastic modeling (Tsallis q-exponential distribution, fractional Lévy noise) and heavy derivations, but is grounded in empirical LOBSTER data with specific fitting and statistical analysis.

  flowchart TD
    Start["Research Goal: Model order flow<br>incorporating power-law<br>properties of cancellation times"]

    Input1["Empirical Financial Data<br>Limit Orders & Cancellations"]

    Method1["Analysis: Discrete Tsallis<br>q-exponential Distribution"]

    Method2["Synthesis: Novel Order Flow Model<br>combining:<br>- Power-law Order Volumes<br>- Power-law Cancellation Times"]

    Outcome1["Finding: Stable Anti-correlation<br>in Order Flow"]
    Outcome2["Application: Framework for<br>Opinion Dynamics in Social Systems"]

    Start --> Input1
    Input1 --> Method1
    Method1 --> Method2
    Method2 --> Outcome1
    Method2 --> Outcome2