解码OTC政府债券市场流动性：一个用于市场动态的ABM模型
Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics
AI总结：
本文开发了一个定制ABM模型，模拟OTC政府债券市场中做市商的互动，研究发现增加代理多样性和降低做市成本能显著提升市场流动性与稳定性。
摘要：
场外交易（OTC）政府债券市场以其双边交易结构为特征，这对理解和确保市场稳定性和流动性提出了独特挑战。在本文中，我们开发了一个定制的ABM（基于代理的模型），模拟风格化政府债券市场内的做市商互动。该模型关注政府债券二级交易中的流动性和稳定性动态，特别是在澳大利亚和英国等集中型市场中。通过此模拟，我们测试了围绕改善市场稳定性的关键假设，重点关注代理多样性、业务成本和客户群规模的影响。我们证明，更大的代理多样性可增强市场流动性，而降低做市成本可改善整体市场稳定性。该模型通过模拟无价格透明度的交易，为计算金融提供了见解，突出了微观结构元素如何影响宏观层面的市场结果。本研究通过运用计算智能技术更好地理解政府债券市场的基本机制，为计算金融这一不断发展的领域做出贡献，为学术界和从业者提供可操作的见解。
英文摘要：
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.
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