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.
Abstract (translated)
通过基于多智能体（multi-agent）的仿真方法探索复杂适应金融交易环境是一种在量化金融领域具有创新性的方法。尽管在具有观测数据的市场中，多智能体强化学习方法占据主导地位，但存在一组由于部分或难以获得数据而具有系统性地重要性的金融市场。因此，我们设计了一种基于元启发式方法的多智能体仿真方法。该方法旨在代表澳大利亚政府债券交易的双边市场，捕捉到银行间交易的双边性质，也称为“场外”(OTC) 交易，以及通常在市场制造商之间发生的双边交易。双边市场的独特性，其特点是有协议的交易和有限的代理数量，为基于智能体的建模和量化金融提供了宝贵的见解。市场结构的固有刚性，与其与全球多边平台和金融市场的分散化相矛盾，强调了我们的基于智能体的模型所提供的独特见解。我们探讨了市场刚性对市场结构和市场设计的影响。这扩展了关于复杂金融交易环境的持续讨论，提供了对它们动态和影响的更深入了解。
URL
https://arxiv.org/abs/2405.02849
