{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T18:10:02Z","timestamp":1755886202647,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T00:00:00Z","timestamp":1700870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,27]]},"DOI":"10.1145\/3604237.3626837","type":"proceedings-article","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T18:09:47Z","timestamp":1700935787000},"page":"253-260","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0690-4866","authenticated-orcid":false,"given":"Nelson","family":"Vadori","sequence":"first","affiliation":[{"name":"J.P. Morgan AI Research, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,25]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1145\/3383455.3422553"},{"key":"e_1_3_2_1_2_1","volume-title":"Learning to Optimize: A Primer and A Benchmark. https:\/\/arxiv.org\/abs\/2103.12828","author":"Chen Tianlong","year":"2021","unstructured":"Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, and Wotao Yin. 2021. Learning to Optimize: A Primer and A Benchmark. https:\/\/arxiv.org\/abs\/2103.12828 (2021)."},{"key":"e_1_3_2_1_3_1","volume-title":"Training Stronger Baselines for Learning to Optimize. NeurIPS","author":"Chen Tianlong","year":"2020","unstructured":"Tianlong Chen, Weiyi Zhang, Jingyang Zhou, Shiyu Chang, Sijia Liu, Lisa Amini, and Zhangyang Wang. 2020. Training Stronger Baselines for Learning to Optimize. NeurIPS (2020)."},{"doi-asserted-by":"crossref","unstructured":"C. Cuchiero W. Khosrawi and J. Teichmann. 2020. A generative adversarial network approach to calibration of local stochastic volatility models. Risks (2020).","key":"e_1_3_2_1_4_1","DOI":"10.3390\/risks8040101"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1016\/j.geb.2011.09.008"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.5555\/3157096.3157336"},{"key":"e_1_3_2_1_7_1","volume-title":"Volatility is rough. Quantitative Finance","author":"Gatheral Jim","year":"2018","unstructured":"Jim Gatheral, Thibault Jaisson, and Mathieu Rosenbaum. 2018. Volatility is rough. Quantitative Finance (2018)."},{"key":"e_1_3_2_1_8_1","volume-title":"Robust pricing and hedging via neural SDEs. https:\/\/arxiv.org\/abs\/2007.04154","author":"Gierjatowicz Patryk","year":"2020","unstructured":"Patryk Gierjatowicz, Mark Sabate-Vidales, David Siska, Lukasz Szpruch, and Zan Zuric. 2020. Robust pricing and hedging via neural SDEs. https:\/\/arxiv.org\/abs\/2007.04154 (2020)."},{"volume-title":"Autonomous Agents and Multiagent Systems","author":"Gupta K","unstructured":"Jayesh\u00a0K Gupta, Maxim Egorov, and Mykel Kochenderfer. 2017. Cooperative Multi-agent Control Using Deep Reinforcement Learning. In Autonomous Agents and Multiagent Systems. Springer International Publishing, 66\u201383.","key":"e_1_3_2_1_9_1"},{"key":"e_1_3_2_1_10_1","volume-title":"Path-dependent volatility. Risk Magazine","author":"Guyon Julien","year":"2014","unstructured":"Julien Guyon. 2014. Path-dependent volatility. Risk Magazine (2014)."},{"key":"e_1_3_2_1_11_1","volume-title":"Being particular about calibration. Risk Magazine","author":"Guyon Julien","year":"2012","unstructured":"Julien Guyon and Pierre Henry-Labordere. 2012. Being particular about calibration. Risk Magazine (2012)."},{"key":"e_1_3_2_1_12_1","volume-title":"Recent Advances in Reinforcement Learning in Finance. https:\/\/arxiv.org\/abs\/2112.04553","author":"Hambly Ben","year":"2021","unstructured":"Ben Hambly, Renyuan Xu, and Huining Yang. 2021. Recent Advances in Reinforcement Learning in Finance. https:\/\/arxiv.org\/abs\/2112.04553 (2021)."},{"key":"e_1_3_2_1_13_1","volume-title":"Equilibria in symmetric games: Theory and applications. Theoretical Economics","author":"Hefti Andreas","year":"2017","unstructured":"Andreas Hefti. 2017. Equilibria in symmetric games: Theory and applications. Theoretical Economics (2017)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1007\/s00780-021-00467-2"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1609\/aaai.v35i6.16696"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a080)","author":"Liang Eric","year":"2018","unstructured":"Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. 2018. RLlib: Abstractions for Distributed Reinforcement Learning. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a080). 3053\u20133062."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1093\/rfs\/14.1.113"},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the 33rd International Conference on International Conference on Machine Learning -","volume":"48","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih, Adri\u00e0\u00a0Puigdom\u00e8nech Badia, Mehdi Mirza, Alex Graves, Tim Harley, Timothy\u00a0P. Lillicrap, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous Methods for Deep Reinforcement Learning. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (New York, NY, USA) (ICML\u201916). JMLR.org, 1928\u20131937."},{"key":"e_1_3_2_1_19_1","volume-title":"High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR","author":"Schulman John","year":"2016","unstructured":"John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2016. High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR (2016)."},{"key":"e_1_3_2_1_20_1","volume-title":"Proximal Policy Optimization Algorithms. https:\/\/arxiv.org\/abs\/1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. https:\/\/arxiv.org\/abs\/1707.06347 (2017)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.1038\/nature16961"},{"key":"e_1_3_2_1_22_1","volume-title":"Reinforcement Learning: An Introduction","author":"Sutton S.","year":"2018","unstructured":"Richard\u00a0S. Sutton and Andrew\u00a0G. Barto. 2018. Reinforcement Learning: An Introduction (second ed.). The MIT Press. http:\/\/incompleteideas.net\/book\/the-book-2nd.html"},{"key":"e_1_3_2_1_23_1","volume-title":"Special Issue on Machine Learning in Finance","author":"Vadori Nelson","year":"2023","unstructured":"Nelson Vadori, Leo Ardon, Sumitra Ganesh, Thomas Spooner, Selim Amrouni, Jared Vann, Mengda Xu, Zeyu Zheng, Tucker Balch, and Manuela Veloso. 2023. Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations. Mathematical Finance, Special Issue on Machine Learning in Finance (2023)."},{"key":"e_1_3_2_1_24_1","volume-title":"Calibration of Shared Equilibria in General Sum Partially Observable Markov Games. Advances in Neural Information Processing Systems (NeurIPS)","author":"Vadori Nelson","year":"2020","unstructured":"Nelson Vadori, Sumitra Ganesh, Prashant Reddy, and Manuela Veloso. 2020. Calibration of Shared Equilibria in General Sum Partially Observable Markov Games. Advances in Neural Information Processing Systems (NeurIPS) (2020)."}],"event":{"acronym":"ICAIF '23","name":"ICAIF '23: 4th ACM International Conference on AI in Finance","location":"Brooklyn NY USA"},"container-title":["4th ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604237.3626837","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3604237.3626837","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:38:00Z","timestamp":1755884280000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604237.3626837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,25]]},"references-count":24,"alternative-id":["10.1145\/3604237.3626837","10.1145\/3604237"],"URL":"https:\/\/doi.org\/10.1145\/3604237.3626837","relation":{},"subject":[],"published":{"date-parts":[[2023,11,25]]},"assertion":[{"value":"2023-11-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}