Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation Study

TL;DR

This paper tackles the challenge of modelling an opaque bilateral bond market by developing a small-scale, inductively calibrated agent-based model (ABM) of four market-maker banks trading a 5-year Australian government bond. Leveraging Sugarscape and MESA-inspired frameworks, the study encodes bilateral trading, client-franchise access, and resource-based welfare rules (including the marginal rate of substitution defined as $MRS_{a,t}= \frac{Accum_{t,a,spice}/Met_{a,spice}}{Accum_{t,a,sugar}/Met_{a,sugar}}$) to explore market stability under varying agent diversity, costs, and client-base breadth. The results emphasize that agent diversity, rather than sheer numbers, stabilizes trading, while reducing agent costs can paradoxically improve stability and liquidity; broadening client bases does not universally enhance welfare. The findings offer regulatory-relevant insights for market design in centralized versus decentralized settings and demonstrate how inductive ABM calibration can illuminate the dynamics of opaque, bilateral markets amid data limitations.

Abstract

Exploring complex adaptive financial trading environments through multi-agent based simulation methods presents an innovative approach within the realm of quantitative finance. Despite the dominance of multi-agent reinforcement learning approaches in financial markets with observable data, there exists a set of systematically significant financial markets that pose challenges due to their partial or obscured data availability. We, therefore, devise a multi-agent simulation approach employing small-scale meta-heuristic methods. This approach aims to represent the opaque bilateral market for Australian government bond trading, capturing the bilateral nature of bank-to-bank trading, also referred to as "over-the-counter" (OTC) trading, and commonly occurring between "market makers". The uniqueness of the bilateral market, characterized by negotiated transactions and a limited number of agents, yields valuable insights for agent-based modelling and quantitative finance. The inherent rigidity of this market structure, which is at odds with the global proliferation of multilateral platforms and the decentralization of finance, underscores the unique insights offered by our agent-based model. We explore the implications of market rigidity on market structure and consider the element of stability, in market design. This extends the ongoing discourse on complex financial trading environments, providing an enhanced understanding of their dynamics and implications.

Figures (3)

• Figure 1: Agent-Based Model Workflow
• Figure 2: Impact of Homogeneity: System instability observed in populations with identical agent features
• Figure 4: Agents with higher vision trade more, and with other agents of higher vision