Paper: arXiv 2501.16331
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.
Complexity vs Empirical Score
- Math Complexity: 4.0/10
- Empirical Rigor: 3.0/10
- Quadrant: Philosophers — conceptual discussion, limited math and data
Why this score: The paper uses agent-based modeling (ABM) with stylized facts rather than heavy formulas, indicating moderate math; its reliance on simulated data without backtesting, public datasets, or statistical metrics results in low empirical rigor.
Research Flowchart
flowchart TD
A["Research Goal: <br>Model OTC Gov Bond Liquidity & Stability"] --> B
subgraph B ["Key Methodology"]
B1["Develop Bespoke Agent-Based Model"]
B2["Simulate Market-Maker Interactions"]
B3["Stylised Concentrated Market Setup"]
end
B --> C["Computational Process: <br>Testing Hypotheses on Agent Diversity & Costs"]
C --> D["Key Findings & Outcomes"]
subgraph D ["Outcomes"]
D1["Greater Agent Diversity → ↑ Liquidity"]
D2["Lower Market-Making Costs → ↑ Stability"]
D3["Micro-structure drives Macro-outputs"]
end
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- Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide (1998)
- A Comparison of Simulation Models Applied to Epidemics (2002)
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- Using surrogate models to calibrate agent-based model parameters under data scarcity (2019)
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- Algorithmic market making in foreign exchange cash markets with hedging and market impact (2021) — link
- A stochastic partial differential equation model for limit order book dynamics (2021)
- Sixteenths or pennies? Observations from a simulation of the NASDAQ stock market (2000)
- The effects of market-making on price dynamics (2008)
- Learning to communicate with deep multi-agent reinforcement learning (2016)
- Deductive and inductive data collection for agent-based modelling: A Comparative Study using a Dairy Supply Chain Case (2016)
- Reinforcement learning for market making in a multi-agent dealer market (2019) — link
- Cooperative multi-agent control using deep reinforcement learning (2017)
- Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018) — link
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- Validation and calibration of an agent-based model: A surrogate approach (2020)
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- A comparison of economic agent-based model calibration methods (2020) — link
- A comprehensive framework for uncovering non‐ linearity and chaos in financial markets: Empirical evidence for four major stock market indices (2020) — link
- Explainability in mechanism design: recent advances and the road ahead (2022)
- Some challenges of calibrating differentiable agent-based models — → 2307.01085
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- Incentivising Market Making in Financial Markets (2022) — link
- The microstructure of the bond market in the 20th century (2019) — link
- Bid-ask spreads and the over-the-counter interdealer markets: Core and peripheral dealers (2019) — link
- Staff Report on Algorithmic Trading in U.S. Capital Markets (2020) — link
- An anatomy of the 2022 gilt market crisis (2023) — link
- Size and Heterogeneity Matter. a Microstructure-Based Analysis of Regulation of Secondary Markets for Government Bonds (2021) — link
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- Liquidity in bond markets - navigating in troubled waters (2022)
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- Signaling in OTC Markets: Benefits and costs of transparency (2020) — link
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- Automation, Intermediation and the Flash Crash (2018) — link
- An application of agent-based modeling to market structure policy: the case of the U.S. Tick Size Pilot Program and market maker profitability (2017) — link
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- BloombergGPT: A Large Language Model for Finance (2023) — link
- People Are Worried About Block Trades (2022)
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- Still the World’s Safe Haven? (2020)
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- Regret Minimization for Security Games With Unobservable Attacker Commitment (2017)
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- Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design (2024) — link
- On the limits of agency in agent-based models (2024) — link
- Can LLMs Generate Random Numbers? Evaluating LLM Sampling in Controlled Domains (2023) — link
- Does GPT-4 Play Dice ? Experiment : Generating only one number each time Experiment : Generating random sequence (2024) — link
- Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation (2024) — link
- TraderTalk: An LLM Behavioural ABM applied to Simulating Human Bilateral Trading Interactions (2024) — link
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- Is Temperature the Creativity Parameter of Large Language Models? (2024) — link
- The Effect of Sampling Temperature on Problem Solving in Large Language Models (2024) — link
- Reasoning with Large Language Models on Graph Tasks: The Influence of Temperature (2024) — link
- Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models (2023) — link
- The performance of large language models on quantitative and verbal ability tests: Initial evidence and implications for unproctored high‐stakes testing (2024) — link
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- Attention is all you need (2017)
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- Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation (2024) — link
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- A model to analyze age-related differences in human-generated head-tail sequences Author summary (2023)
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- Options, Futures, and Other Derivatives (2022)
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- A Comparison of Large Language Model and Human Performance on Random Number Generation Tasks (2024) — link
- The recency effect: Implicit learning with explicit retrieval? (1993) — link
- The Effect of Context and Individual Differences in Human-Generated Randomness (2021) — link
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- How Random is Random? Evaluating the Randomness and Humaness of LLMs’ Coin Flips (2024) — link
1 of 469 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.