[3Xin2] Poster session 1

[3Xin2-01] Impact of the number of AI traders on the market

Micro-foundations of the GARCH model

〇Kei Nakagawa¹, Masanori Hirano², Kentaro Minami³, Takanobu Mizuta⁴ (1.Nomura Asset Management Co., Ltd., 2.Preferred Networks, Inc., 3.PayPay Corporation, 4.SPARX Asset Management Co., Ltd.)

Keywords:Multi Agent, Financial Market, Micro Foundation

Recent developments of machine learning techniques have made AI traders more prominent in financial markets, drawing attention to their market impact.
We focus on the GARCH(1,1) model, a key financial time series model, to analyze the influence of AI traders.
The GARCH model is the most common method for modeling conditional variance capable of replicating volatility clustering, but its micro-foundations have not yet been fully understood.
We categorize market investors into noise traders, fundamental traders, and AI traders and construct the GARCH model with artificial markets using them.
We explore how each group affects the GARCH model's parameters and examine the role of AI traders in market dynamics and volatility, using theoretical models and simulations.