Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics
This research addresses market stability and liquidity challenges for practitioners and academics in computational finance, though it is incremental as it applies existing agent-based modeling techniques to a specific domain.
The paper tackled the problem of understanding liquidity and stability in over-the-counter government bond markets by developing an agent-based model to simulate market-maker interactions, demonstrating that greater agent diversity enhances liquidity and reducing market-making costs improves stability.
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.