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

• URL: http://arxiv.org/abs/2203.06865v4
• Date: Fri, 6 Oct 2023 12:48:48 GMT
• Title: Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective
• Authors: Nelson Vadori
• Abstract summary: 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. 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.
• Score: 3.626013617212667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textit{et} Henry-Labordere where particles, instead of being designed to ensure $\sigma_{loc}(t,S_t)^2 = \mathbb{E}[\sigma_t^2|S_t]$, are learning RL-driven agents cooperating towards more general calibration targets.

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