A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets -- A New Microfoundations of GARCH model

Paper: arXiv 2409.12516

Abstract

The AI traders in financial markets have sparked significant interest in their effects on price formation mechanisms and market volatility, raising important questions for market stability and regulation. Despite this interest, a comprehensive model to quantitatively assess the specific impacts of AI traders remains undeveloped. This study aims to address this gap by modeling the influence of AI traders on market price formation and volatility within a multi-agent framework, leveraging the concept of microfoundations. Microfoundations involve understanding macroeconomic phenomena, such as market price formation, through the decision-making and interactions of individual economic agents. While widely acknowledged in macroeconomics, microfoundational approaches remain unexplored in empirical finance, particularly for models like the GARCH model, which captures key financial statistical properties such as volatility clustering and fat tails. This study proposes a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. By mathematically aggregating the micro-structure of these agents, we establish the microfoundations of the GARCH model. We validate this model through multi-agent simulations, confirming its ability to reproduce the stylized facts of financial markets. Finally, we analyze the impact of AI traders using parameters derived from these microfoundations, contributing to a deeper understanding of their role in market dynamics.

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics, including a multi-agent aggregation derivation to establish microfoundations for the GARCH model, which is a complex theoretical exercise. It includes multi-agent simulations to validate stylized facts, providing empirical grounding, though it lacks direct backtesting on real financial data.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>'Model AI Traders' impact on<br/>price formation & market volatility'"] --> B["Key Methodology<br/>Multi-agent Market Model"]
  B --> C["Model Components<br/>Noise Traders<br/>Fundamental Traders<br/>AI Traders"]
  C --> D{"Computational Processes"}
  D --> E["Agent Interactions<br/>+ Micro-structure Aggregation"]
  D --> F["Multi-agent Simulations<br/>+ Mathematical Derivation"]
  E --> G["Outcomes<br/>Derive Microfoundations of GARCH<br/>Validate Stylized Facts"]
  F --> G
  G --> H["Final Analysis<br/>Quantify Impact of AI Traders on Volatility"]

Cited References (38)

1. A simulation analysis of the microstructure of double auction markets (2002) — link
2. The properties of equally weighted risk contribution portfolios (2010)
3. Generalized autoregressive conditional heteroskedasticity (1986)
4. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation (1982)
5. The theory of portfolio selection (1965)
6. Micro-foundation of ARCH model (2016)
7. Agent-based economic models and econometrics (2012)
8. Empirical properties of asset returns: stylized facts and statistical issues (2001)

0 of 38 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.