Volatility-Volume Order Slicing via Statistical Analysis

ArXiv ID: 2412.12482 “View on arXiv”

Authors: Unknown

Abstract

This paper addresses the challenges faced in large-volume trading, where executing substantial orders can result in significant market impact and slippage. To mitigate these effects, this study proposes a volatility-volume-based order slicing strategy that leverages Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations. These methods are used to dynamically estimate future trading volumes and price ranges, enabling traders to adapt their strategies by segmenting order execution sizes based on these predictions. Results show that the proposed approach improves trade execution efficiency, reduces market impact, and offers a more adaptive solution for volatile market conditions. The findings have practical implications for large-volume trading, providing a foundation for further research into adaptive execution strategies.

Keywords: Order Execution, Market Impact, Volatility Modeling, Markov Chain Monte Carlo, Algorithmic Trading

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical methods including MLE, Metropolis-Hastings MCMC, and EWMA, indicating moderate-to-high mathematical complexity, while the implementation uses real financial data (Tesla intraday) with detailed preprocessing, aggregation, and empirical results, demonstrating substantial empirical rigor.

  flowchart TD
    A["Research Goal:<br>Optimize Large-Volume Trade Execution<br>to Reduce Market Impact & Slippage"] --> B["Data Inputs:<br>Historical Volatility & Volume Data"]
    B --> C["Key Methodology:<br>Statistical Analysis & Modeling"]
    C --> D{"Computational Processes"}
    D --> E["EWMA:<br>Dynamic Volume Prediction"]
    D --> F["MCMC:<br>Price Range Simulation"]
    E & F --> G["Strategy Execution:<br>Volatility-Volume Based Order Slicing"]
    G --> H["Key Findings:<br>Improved Efficiency, Reduced Impact,<br>Adaptive to Volatile Markets"]