Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics
Alicia Vidler, Toby Walsh
TL;DR
This paper tackles the problem of understanding liquidity and stability in OTC government bond markets, where data are sparse and trading is bilateral. It introduces a Sugarscape-inspired ABM to simulate market-maker interactions, calibrating it to the Australian government-bond market to study how agent diversity, client breadth, and operating costs shape liquidity. Key findings show that heterogeneity among market makers enhances trading activity and stability more effectively than merely increasing agent numbers, while lower costs further improve outcomes; extreme reductions in diversity or sharp cost increases can destabilize trading. The work offers a computational approach with practical implications for policy and regulation, illustrating how micro-structural features influence macro-level market dynamics in OTC bond markets.
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.