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Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective

arXiv 2203.06865 ↗First posted 14 Mar 2022q-fin.CP cs.AI cs.LGcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options. Traditionally, one employs a mix of intuition, theoretical and empirical analysis to find models that achieve exact or approximate fits. Our contribution is to show how a suitable game theoretical formulation of this problem can help solve this question by leveraging existing developments in modern deep multi-agent reinforcement learning to search in the space of stochastic processes. Our experiments show that we are able to learn local volatility, as well as path-dependence required in the volatility process to minimize the price of a Bermudan option. Our algorithm can be seen as a particle method textit`a la Guyon textitet Henry-Labordere where particles, instead of being designed to ensure σloc(t,St)2 = mathbbE[σt2|St], are learning RL-driven agents cooperating towards more general calibration targets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 12 of its 33 references that are in the map. First posted on arXiv 14 Mar 2022.

Builds on

12 of 33 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  4. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  5. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  6. 2021Recent Advances in Reinforcement Learning in Financecited by 102
  7. 2019Differentiable Game Mechanicscited by 43
  8. 2020EigenGame: PCA as a Nash Equilibriumcited by 39
  9. 2020Training Stronger Baselines for Learning to Optimizecited by 31
  10. 2020A generative adversarial network approach to calibration of local stochastic volatility modelscited by 28
  11. 2020Robust pricing and hedging via neural SDEscited by 21
  12. 2020Calibration of Shared Equilibria in General Sum Partially Observable Markov Gamescited by 12

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