---
title: ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility
url: https://www.ml-quant.com/papers/doi/10-1145-3677052-3698695/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: doi:10.1145/3677052.3698695
source_url: http://dx.doi.org/10.1145/3677052.3698695
featured: 2025-08-29
citations: 3
topic: Derivatives & Volatility
---


# ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility

The study combines Adversarial Reinforcement Learning, Hawkes Processes, and variable volatility to enhance market-making strategies, showing improved adaptability in high-volatility conditions and better market simulations.

- Source: http://dx.doi.org/10.1145/3677052.3698695
- Identifier: doi:10.1145/3677052.3698695
- Released: 2025-08-07
- First featured: Quant Letter No. 111 (2025-08-29): https://www.ml-quant.com/issues/2025-08-29/
- Citations (Semantic Scholar): 3
- Published in: Proceedings of the 5th ACM International Conference on AI in Finance
- Topic: Derivatives & Volatility

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