The Deep Parametric PDE Method: Application to Option Pricing

Kathrin Glau and Linus Wunderlich

Papers from arXiv.org

Abstract: We propose the deep parametric PDE method to solve high-dimensional parametric partial differential equations. A single neural network approximates the solution of a whole family of PDEs after being trained without the need of sample solutions. As a practical application, we compute option prices in the multivariate Black-Scholes model. After a single training phase, the prices for different time, state and model parameters are available in milliseconds. We evaluate the accuracy in the price and a generalisation of the implied volatility with examples of up to 25 dimensions. A comparison with alternative machine learning approaches, confirms the effectiveness of the approach.

Date: 2020-12
New Economics Papers: this item is included in nep-big and nep-cmp
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