Dealer Strategies in Agent-Based Models

ArXiv ID: 2312.05943 “View on arXiv”

Authors: Unknown

Abstract

This paper explores the utility of agent-based simulations in realistically modelling market structures and sheds light on the nuances of optimal dealer strategies. It underscores the contrast between conclusions drawn from probabilistic modelling and agent-based simulations, but also highlights the importance of employing a realistic test bed to analyse intricate dynamics. This is achieved by extending the agent-based model for auction markets by \cite{“Chiarella.2008”} to include liquidity providers. By constantly and passively quoting, the dealers influence their own wealth but also have ramifications on the market as a whole and the other participating agents. Through synthetic market simulations, the optimal behaviour of different dealer strategies and their consequences on market dynamics are examined. The analysis reveals that dealers exhibiting greater risk aversion tend to yield better performance outcomes. The choice of quote sizes by dealers is strategy-dependent: one strategy demonstrates enhanced performance with larger quote sizes, whereas the other strategy show a better results with smaller ones. Increasing quote size shows positive influence on the market in terms of volatility and kurtosis with both dealer strategies. However, the impact stemming from larger risk aversion is mixed. While one of the dealer strategies shows no discernible effect, the other strategy results in mixed outcomes, encompassing both positive and negative effects.

Keywords: Agent-Based Modeling, Dealer Strategies, Liquidity Provision, Market Microstructure

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced agent-based modeling, stochastic processes, and utility optimization frameworks, indicating high mathematical density; however, it relies entirely on synthetic simulations without real-world data or implementation details like code or backtests, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal<br>Determine optimal dealer strategies<br>and market impact in agent-based models"] --> B["Methodology<br>Extend Chiarella.2008 auction model<br>to include liquidity providers"]

    B --> C["Input: Synthetic Market Simulations"]

    C --> D{"Computational Process"}

    D --> E["Strategy A: Passive Quoting<br>Large Quote Size"]
    D --> F["Strategy B: Passive Quoting<br>Small Quote Size"]

    E & F --> G["Key Findings"]

    G --> H["Risk Aversion<br>High risk aversion improves performance"]
    G --> I["Quote Size Impact<br>Strategy A: Large size optimal<br>Strategy B: Small size optimal"]
    G --> J["Market Dynamics<br>Increased quote size reduces volatility<br>Risk aversion impact: Mixed/Neutral"]