Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics

ArXiv ID: 2501.16331 “View on arXiv”

Authors: Unknown

Abstract

The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market. The model focuses on the dynamics of liquidity and stability in the secondary trading of government bonds, particularly in concentrated markets like those found in Australia and the UK. Through this simulation, we test key hypotheses around improving market stability, focusing on the effects of agent diversity, business costs, and client base size. We demonstrate that greater agent diversity enhances market liquidity and that reducing the costs of market-making can improve overall market stability. The model offers insights into computational finance by simulating trading without price transparency, highlighting how micro-structural elements can affect macro-level market outcomes. This research contributes to the evolving field of computational finance by employing computational intelligence techniques to better understand the fundamental mechanics of government bond markets, providing actionable insights for both academics and practitioners.

Keywords: agent-based modeling, government bonds, market liquidity, market making, OTC markets, Fixed Income

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper uses agent-based modeling (ABM) with stylized facts rather than heavy formulas, indicating moderate math; its reliance on simulated data without backtesting, public datasets, or statistical metrics results in low empirical rigor.

  flowchart TD
    A["Research Goal: <br>Model OTC Gov Bond Liquidity & Stability"] --> B
    subgraph B ["Key Methodology"]
        B1["Develop Bespoke Agent-Based Model"]
        B2["Simulate Market-Maker Interactions"]
        B3["Stylised Concentrated Market Setup"]
    end
    B --> C["Computational Process: <br>Testing Hypotheses on Agent Diversity & Costs"]
    C --> D["Key Findings & Outcomes"]
    subgraph D ["Outcomes"]
        D1["Greater Agent Diversity → ↑ Liquidity"]
        D2["Lower Market-Making Costs → ↑ Stability"]
        D3["Micro-structure drives Macro-outputs"]
    end