Accelerated American option pricing with deep neural networks

David Anderson; Urban Ulrych; David Anderson; Urban Ulrych

doi:10.3934/QFE.2023011

Quantitative Finance and Economics

2023, Volume 7, Issue 2: 207-228. doi: 10.3934/QFE.2023011

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Accelerated American option pricing with deep neural networks

David Anderson ¹,
Urban Ulrych ^{1,2,3
,
,}

1.
Department of Banking and Finance, University of Zurich, Zurich, Switzerland
2.
College of Management of Technology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
3.
Swiss Finance Institute, Geneva, Switzerland

Received: 01 March 2023 Revised: 20 April 2023 Accepted: 23 April 2023 Published: 04 May 2023
JEL Codes: C45, C63, G13

Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a method for accelerating the pricing of American options to near-instantaneous using a feed-forward neural network. This neural network is trained over the chosen (e.g., Heston) stochastic volatility specification. Such an approach facilitates parameter interpretability, as generally required by the regulators, and establishes our method in the area of eXplainable Artificial Intelligence (XAI) for finance. We show that the proposed deep explainable pricer induces a speed-accuracy trade-off compared to the typical Monte Carlo or Partial Differential Equation-based pricing methods. Moreover, the proposed approach allows for pricing derivatives with path-dependent and more complex payoffs and is, given the sufficient accuracy of computation and its tractable nature, applicable in a market-making environment.
- American option pricing,
- deep neural networks,
- explainable artificial intelligence,
- parametric option pricing,
- speed-accuracy trade-off,
- market making
Citation: David Anderson, Urban Ulrych. Accelerated American option pricing with deep neural networks[J]. Quantitative Finance and Economics, 2023, 7(2): 207-228. doi: 10.3934/QFE.2023011

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Abstract

Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a method for accelerating the pricing of American options to near-instantaneous using a feed-forward neural network. This neural network is trained over the chosen (e.g., Heston) stochastic volatility specification. Such an approach facilitates parameter interpretability, as generally required by the regulators, and establishes our method in the area of eXplainable Artificial Intelligence (XAI) for finance. We show that the proposed deep explainable pricer induces a speed-accuracy trade-off compared to the typical Monte Carlo or Partial Differential Equation-based pricing methods. Moreover, the proposed approach allows for pricing derivatives with path-dependent and more complex payoffs and is, given the sufficient accuracy of computation and its tractable nature, applicable in a market-making environment.

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